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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 computationWed, 25 Nov 2009 10:44:30 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/25/t1259171201rxzeiixgpinoanm.htm/, Retrieved Thu, 02 May 2024 18:30:29 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=59518, Retrieved Thu, 02 May 2024 18:30:29 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact199

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [] [2009-11-18 16:22:43] [90f6d58d515a4caed6fb4b8be4e11eaa]
- R PD      [Multiple Regression] [WS07 - ReviewModel2] [2009-11-25 17:23:59] [df6326eec97a6ca984a853b142930499]
-    D          [Multiple Regression] [WS07 - ReviewModel3] [2009-11-25 17:44:30] [0cc924834281808eda7297686c82928f] [Current]
-   PD            [Multiple Regression] [WS07 - ReviewModel4] [2009-11-25 17:56:59] [df6326eec97a6ca984a853b142930499]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
7.70	110.30	8.10	8.00
7.50	103.90	7.70	8.10
7.60	101.60	7.50	7.70
7.80	94.60	7.60	7.50
7.80	95.90	7.80	7.60
7.80	104.70	7.80	7.80
7.50	102.80	7.80	7.80
7.50	98.10	7.50	7.80
7.10	113.90	7.50	7.50
7.50	80.90	7.10	7.50
7.50	95.70	7.50	7.10
7.60	113.20	7.50	7.50
7.70	105.90	7.60	7.50
7.70	108.80	7.70	7.60
7.90	102.30	7.70	7.70
8.10	99.00	7.90	7.70
8.20	100.70	8.10	7.90
8.20	115.50	8.20	8.10
8.20	100.70	8.20	8.20
7.90	109.90	8.20	8.20
7.30	114.60	7.90	8.20
6.90	85.40	7.30	7.90
6.60	100.50	6.90	7.30
6.70	114.80	6.60	6.90
6.90	116.50	6.70	6.60
7.00	112.90	6.90	6.70
7.10	102.00	7.00	6.90
7.20	106.00	7.10	7.00
7.10	105.30	7.20	7.10
6.90	118.80	7.10	7.20
7.00	106.10	6.90	7.10
6.80	109.30	7.00	6.90
6.40	117.20	6.80	7.00
6.70	92.50	6.40	6.80
6.60	104.20	6.70	6.40
6.40	112.50	6.60	6.70
6.30	122.40	6.40	6.60
6.20	113.30	6.30	6.40
6.50	100.00	6.20	6.30
6.80	110.70	6.50	6.20
6.80	112.80	6.80	6.50
6.40	109.80	6.80	6.80
6.10	117.30	6.40	6.80
5.80	109.10	6.10	6.40
6.10	115.90	5.80	6.10
7.20	96.00	6.10	5.80
7.30	99.80	7.20	6.10
6.90	116.80	7.30	7.20
6.10	115.70	6.90	7.30
5.80	99.40	6.10	6.90
6.20	94.30	5.80	6.10
7.10	91.00	6.20	5.80
7.70	93.20	7.10	6.20
7.90	103.10	7.70	7.10
7.70	94.10	7.90	7.70
7.40	91.80	7.70	7.90
7.50	102.70	7.40	7.70
8.00	82.60	7.50	7.40

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59518&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59518&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59518&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Wman[t] = + 4.06135842792089 -0.0176255021967389Ecogr[t] + 1.23336275067173`Wmant-1`[t] -0.525370089262143`Wmant-2`[t] -0.00435524001472917t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Wman[t] =  +  4.06135842792089 -0.0176255021967389Ecogr[t] +  1.23336275067173`Wmant-1`[t] -0.525370089262143`Wmant-2`[t] -0.00435524001472917t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59518&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Wman[t] =  +  4.06135842792089 -0.0176255021967389Ecogr[t] +  1.23336275067173`Wmant-1`[t] -0.525370089262143`Wmant-2`[t] -0.00435524001472917t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59518&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59518&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
Wman[t] = + 4.06135842792089 -0.0176255021967389Ecogr[t] + 1.23336275067173`Wmant-1`[t] -0.525370089262143`Wmant-2`[t] -0.00435524001472917t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.061358427920890.6364876.380900
Ecogr-0.01762550219673890.003321-5.30792e-061e-06
`Wmant-1`1.233362750671730.09856812.512800
`Wmant-2`-0.5253700892621430.100618-5.22143e-062e-06
t-0.004355240014729170.002285-1.90580.0621030.031051

\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) & 4.06135842792089 & 0.636487 & 6.3809 & 0 & 0 \tabularnewline
Ecogr & -0.0176255021967389 & 0.003321 & -5.3079 & 2e-06 & 1e-06 \tabularnewline
`Wmant-1` & 1.23336275067173 & 0.098568 & 12.5128 & 0 & 0 \tabularnewline
`Wmant-2` & -0.525370089262143 & 0.100618 & -5.2214 & 3e-06 & 2e-06 \tabularnewline
t & -0.00435524001472917 & 0.002285 & -1.9058 & 0.062103 & 0.031051 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59518&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]4.06135842792089[/C][C]0.636487[/C][C]6.3809[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Ecogr[/C][C]-0.0176255021967389[/C][C]0.003321[/C][C]-5.3079[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]`Wmant-1`[/C][C]1.23336275067173[/C][C]0.098568[/C][C]12.5128[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Wmant-2`[/C][C]-0.525370089262143[/C][C]0.100618[/C][C]-5.2214[/C][C]3e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]t[/C][C]-0.00435524001472917[/C][C]0.002285[/C][C]-1.9058[/C][C]0.062103[/C][C]0.031051[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59518&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59518&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)4.061358427920890.6364876.380900
Ecogr-0.01762550219673890.003321-5.30792e-061e-06
`Wmant-1`1.233362750671730.09856812.512800
`Wmant-2`-0.5253700892621430.100618-5.22143e-062e-06
t-0.004355240014729170.002285-1.90580.0621030.031051






Multiple Linear Regression - Regression Statistics
Multiple R0.93742486492907
R-squared0.878765377387285
Adjusted R-squared0.86961559454859
F-TEST (value)96.0422113704042
F-TEST (DF numerator)4
F-TEST (DF denominator)53
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.233917248026228
Sum Squared Residuals2.90001578298070

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.93742486492907 \tabularnewline
R-squared & 0.878765377387285 \tabularnewline
Adjusted R-squared & 0.86961559454859 \tabularnewline
F-TEST (value) & 96.0422113704042 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 53 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.233917248026228 \tabularnewline
Sum Squared Residuals & 2.90001578298070 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59518&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.93742486492907[/C][/ROW]
[ROW][C]R-squared[/C][C]0.878765377387285[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.86961559454859[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]96.0422113704042[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]53[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.233917248026228[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2.90001578298070[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59518&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59518&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 R0.93742486492907
R-squared0.878765377387285
Adjusted R-squared0.86961559454859
F-TEST (value)96.0422113704042
F-TEST (DF numerator)4
F-TEST (DF denominator)53
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.233917248026228
Sum Squared Residuals2.90001578298070






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
17.77.9001878619497-0.200187861949702
27.57.462753726799190.0372462732008101
37.67.462412627407470.137587372592530
47.87.80984619568952-0.00984619568951574
57.87.97671334402716-0.176713344027157
67.87.71217966682870.0878203331713034
77.57.74131288098777-0.241312880987771
87.57.44978867609620.0502113239038022
97.17.32456152815164-0.224561528151636
107.57.40850276036060.091497239639399
117.57.84678322380768-0.346783223807684
127.67.323833659645170.276166340354834
137.77.57148086073380.128519139266197
147.77.586810930489490.11318906951051
157.97.644484445827350.255515554172651
168.17.94496591319620.155034086803795
178.28.052245851728930.147754148271065
188.27.805295436417210.394704563582787
198.28.0092606199880.190739380011994
207.97.842750759763280.0572492402367223
217.37.38554683422236-0.0855468342223598
226.97.31344963472801-0.413449634728013
236.66.86482626483112-0.264826264831124
246.76.448565553906370.251434446093634
256.96.6951942620030.204805737997003
2676.948426371104660.0515736288953413
277.17.15445136224913-0.0544513622491277
287.27.15039337958840.0496066204115997
297.17.22917525725235-0.129175257252348
306.96.811002453588260.0889975464117454
3176.836355550263980.163644449736019
326.87.00400899613929-0.204008996139287
336.46.56120272970976-0.161202729709761
346.76.603926311538220.0960736884617774
356.66.97350955672802-0.373509556728023
366.46.54191534663454-0.141915346634544
376.36.168932093663970.131067906336030
386.26.30670666642482-0.106706666424820
396.56.465971339485760.0340286605142388
406.86.695569060093660.104430939906343
416.86.86659806388865-0.0665980638886508
426.46.7575083036855-0.357508303685495
436.16.12761669692653-0.0276166969265347
445.86.1079297854284-0.307929785428403
456.15.771323332052980.328676667947025
467.26.645335437733510.554664562266489
477.37.77309128833143-0.47309128833143
486.97.01453168785095-0.114531687850953
496.16.48368239105773-0.383682391057733
505.85.99008067201732-0.190080672017324
516.26.125902739414160.0740972605858393
527.16.8306677836960.269332216303996
537.77.687414878748140.0125851212518561
547.97.775751737052810.124248262947193
557.77.86147651338579-0.161476513385787
567.47.54591336043678-0.145913360436783
577.57.084505339128510.415494660871489
5887.715369995114050.28463000488595

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 7.7 & 7.9001878619497 & -0.200187861949702 \tabularnewline
2 & 7.5 & 7.46275372679919 & 0.0372462732008101 \tabularnewline
3 & 7.6 & 7.46241262740747 & 0.137587372592530 \tabularnewline
4 & 7.8 & 7.80984619568952 & -0.00984619568951574 \tabularnewline
5 & 7.8 & 7.97671334402716 & -0.176713344027157 \tabularnewline
6 & 7.8 & 7.7121796668287 & 0.0878203331713034 \tabularnewline
7 & 7.5 & 7.74131288098777 & -0.241312880987771 \tabularnewline
8 & 7.5 & 7.4497886760962 & 0.0502113239038022 \tabularnewline
9 & 7.1 & 7.32456152815164 & -0.224561528151636 \tabularnewline
10 & 7.5 & 7.4085027603606 & 0.091497239639399 \tabularnewline
11 & 7.5 & 7.84678322380768 & -0.346783223807684 \tabularnewline
12 & 7.6 & 7.32383365964517 & 0.276166340354834 \tabularnewline
13 & 7.7 & 7.5714808607338 & 0.128519139266197 \tabularnewline
14 & 7.7 & 7.58681093048949 & 0.11318906951051 \tabularnewline
15 & 7.9 & 7.64448444582735 & 0.255515554172651 \tabularnewline
16 & 8.1 & 7.9449659131962 & 0.155034086803795 \tabularnewline
17 & 8.2 & 8.05224585172893 & 0.147754148271065 \tabularnewline
18 & 8.2 & 7.80529543641721 & 0.394704563582787 \tabularnewline
19 & 8.2 & 8.009260619988 & 0.190739380011994 \tabularnewline
20 & 7.9 & 7.84275075976328 & 0.0572492402367223 \tabularnewline
21 & 7.3 & 7.38554683422236 & -0.0855468342223598 \tabularnewline
22 & 6.9 & 7.31344963472801 & -0.413449634728013 \tabularnewline
23 & 6.6 & 6.86482626483112 & -0.264826264831124 \tabularnewline
24 & 6.7 & 6.44856555390637 & 0.251434446093634 \tabularnewline
25 & 6.9 & 6.695194262003 & 0.204805737997003 \tabularnewline
26 & 7 & 6.94842637110466 & 0.0515736288953413 \tabularnewline
27 & 7.1 & 7.15445136224913 & -0.0544513622491277 \tabularnewline
28 & 7.2 & 7.1503933795884 & 0.0496066204115997 \tabularnewline
29 & 7.1 & 7.22917525725235 & -0.129175257252348 \tabularnewline
30 & 6.9 & 6.81100245358826 & 0.0889975464117454 \tabularnewline
31 & 7 & 6.83635555026398 & 0.163644449736019 \tabularnewline
32 & 6.8 & 7.00400899613929 & -0.204008996139287 \tabularnewline
33 & 6.4 & 6.56120272970976 & -0.161202729709761 \tabularnewline
34 & 6.7 & 6.60392631153822 & 0.0960736884617774 \tabularnewline
35 & 6.6 & 6.97350955672802 & -0.373509556728023 \tabularnewline
36 & 6.4 & 6.54191534663454 & -0.141915346634544 \tabularnewline
37 & 6.3 & 6.16893209366397 & 0.131067906336030 \tabularnewline
38 & 6.2 & 6.30670666642482 & -0.106706666424820 \tabularnewline
39 & 6.5 & 6.46597133948576 & 0.0340286605142388 \tabularnewline
40 & 6.8 & 6.69556906009366 & 0.104430939906343 \tabularnewline
41 & 6.8 & 6.86659806388865 & -0.0665980638886508 \tabularnewline
42 & 6.4 & 6.7575083036855 & -0.357508303685495 \tabularnewline
43 & 6.1 & 6.12761669692653 & -0.0276166969265347 \tabularnewline
44 & 5.8 & 6.1079297854284 & -0.307929785428403 \tabularnewline
45 & 6.1 & 5.77132333205298 & 0.328676667947025 \tabularnewline
46 & 7.2 & 6.64533543773351 & 0.554664562266489 \tabularnewline
47 & 7.3 & 7.77309128833143 & -0.47309128833143 \tabularnewline
48 & 6.9 & 7.01453168785095 & -0.114531687850953 \tabularnewline
49 & 6.1 & 6.48368239105773 & -0.383682391057733 \tabularnewline
50 & 5.8 & 5.99008067201732 & -0.190080672017324 \tabularnewline
51 & 6.2 & 6.12590273941416 & 0.0740972605858393 \tabularnewline
52 & 7.1 & 6.830667783696 & 0.269332216303996 \tabularnewline
53 & 7.7 & 7.68741487874814 & 0.0125851212518561 \tabularnewline
54 & 7.9 & 7.77575173705281 & 0.124248262947193 \tabularnewline
55 & 7.7 & 7.86147651338579 & -0.161476513385787 \tabularnewline
56 & 7.4 & 7.54591336043678 & -0.145913360436783 \tabularnewline
57 & 7.5 & 7.08450533912851 & 0.415494660871489 \tabularnewline
58 & 8 & 7.71536999511405 & 0.28463000488595 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59518&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]7.7[/C][C]7.9001878619497[/C][C]-0.200187861949702[/C][/ROW]
[ROW][C]2[/C][C]7.5[/C][C]7.46275372679919[/C][C]0.0372462732008101[/C][/ROW]
[ROW][C]3[/C][C]7.6[/C][C]7.46241262740747[/C][C]0.137587372592530[/C][/ROW]
[ROW][C]4[/C][C]7.8[/C][C]7.80984619568952[/C][C]-0.00984619568951574[/C][/ROW]
[ROW][C]5[/C][C]7.8[/C][C]7.97671334402716[/C][C]-0.176713344027157[/C][/ROW]
[ROW][C]6[/C][C]7.8[/C][C]7.7121796668287[/C][C]0.0878203331713034[/C][/ROW]
[ROW][C]7[/C][C]7.5[/C][C]7.74131288098777[/C][C]-0.241312880987771[/C][/ROW]
[ROW][C]8[/C][C]7.5[/C][C]7.4497886760962[/C][C]0.0502113239038022[/C][/ROW]
[ROW][C]9[/C][C]7.1[/C][C]7.32456152815164[/C][C]-0.224561528151636[/C][/ROW]
[ROW][C]10[/C][C]7.5[/C][C]7.4085027603606[/C][C]0.091497239639399[/C][/ROW]
[ROW][C]11[/C][C]7.5[/C][C]7.84678322380768[/C][C]-0.346783223807684[/C][/ROW]
[ROW][C]12[/C][C]7.6[/C][C]7.32383365964517[/C][C]0.276166340354834[/C][/ROW]
[ROW][C]13[/C][C]7.7[/C][C]7.5714808607338[/C][C]0.128519139266197[/C][/ROW]
[ROW][C]14[/C][C]7.7[/C][C]7.58681093048949[/C][C]0.11318906951051[/C][/ROW]
[ROW][C]15[/C][C]7.9[/C][C]7.64448444582735[/C][C]0.255515554172651[/C][/ROW]
[ROW][C]16[/C][C]8.1[/C][C]7.9449659131962[/C][C]0.155034086803795[/C][/ROW]
[ROW][C]17[/C][C]8.2[/C][C]8.05224585172893[/C][C]0.147754148271065[/C][/ROW]
[ROW][C]18[/C][C]8.2[/C][C]7.80529543641721[/C][C]0.394704563582787[/C][/ROW]
[ROW][C]19[/C][C]8.2[/C][C]8.009260619988[/C][C]0.190739380011994[/C][/ROW]
[ROW][C]20[/C][C]7.9[/C][C]7.84275075976328[/C][C]0.0572492402367223[/C][/ROW]
[ROW][C]21[/C][C]7.3[/C][C]7.38554683422236[/C][C]-0.0855468342223598[/C][/ROW]
[ROW][C]22[/C][C]6.9[/C][C]7.31344963472801[/C][C]-0.413449634728013[/C][/ROW]
[ROW][C]23[/C][C]6.6[/C][C]6.86482626483112[/C][C]-0.264826264831124[/C][/ROW]
[ROW][C]24[/C][C]6.7[/C][C]6.44856555390637[/C][C]0.251434446093634[/C][/ROW]
[ROW][C]25[/C][C]6.9[/C][C]6.695194262003[/C][C]0.204805737997003[/C][/ROW]
[ROW][C]26[/C][C]7[/C][C]6.94842637110466[/C][C]0.0515736288953413[/C][/ROW]
[ROW][C]27[/C][C]7.1[/C][C]7.15445136224913[/C][C]-0.0544513622491277[/C][/ROW]
[ROW][C]28[/C][C]7.2[/C][C]7.1503933795884[/C][C]0.0496066204115997[/C][/ROW]
[ROW][C]29[/C][C]7.1[/C][C]7.22917525725235[/C][C]-0.129175257252348[/C][/ROW]
[ROW][C]30[/C][C]6.9[/C][C]6.81100245358826[/C][C]0.0889975464117454[/C][/ROW]
[ROW][C]31[/C][C]7[/C][C]6.83635555026398[/C][C]0.163644449736019[/C][/ROW]
[ROW][C]32[/C][C]6.8[/C][C]7.00400899613929[/C][C]-0.204008996139287[/C][/ROW]
[ROW][C]33[/C][C]6.4[/C][C]6.56120272970976[/C][C]-0.161202729709761[/C][/ROW]
[ROW][C]34[/C][C]6.7[/C][C]6.60392631153822[/C][C]0.0960736884617774[/C][/ROW]
[ROW][C]35[/C][C]6.6[/C][C]6.97350955672802[/C][C]-0.373509556728023[/C][/ROW]
[ROW][C]36[/C][C]6.4[/C][C]6.54191534663454[/C][C]-0.141915346634544[/C][/ROW]
[ROW][C]37[/C][C]6.3[/C][C]6.16893209366397[/C][C]0.131067906336030[/C][/ROW]
[ROW][C]38[/C][C]6.2[/C][C]6.30670666642482[/C][C]-0.106706666424820[/C][/ROW]
[ROW][C]39[/C][C]6.5[/C][C]6.46597133948576[/C][C]0.0340286605142388[/C][/ROW]
[ROW][C]40[/C][C]6.8[/C][C]6.69556906009366[/C][C]0.104430939906343[/C][/ROW]
[ROW][C]41[/C][C]6.8[/C][C]6.86659806388865[/C][C]-0.0665980638886508[/C][/ROW]
[ROW][C]42[/C][C]6.4[/C][C]6.7575083036855[/C][C]-0.357508303685495[/C][/ROW]
[ROW][C]43[/C][C]6.1[/C][C]6.12761669692653[/C][C]-0.0276166969265347[/C][/ROW]
[ROW][C]44[/C][C]5.8[/C][C]6.1079297854284[/C][C]-0.307929785428403[/C][/ROW]
[ROW][C]45[/C][C]6.1[/C][C]5.77132333205298[/C][C]0.328676667947025[/C][/ROW]
[ROW][C]46[/C][C]7.2[/C][C]6.64533543773351[/C][C]0.554664562266489[/C][/ROW]
[ROW][C]47[/C][C]7.3[/C][C]7.77309128833143[/C][C]-0.47309128833143[/C][/ROW]
[ROW][C]48[/C][C]6.9[/C][C]7.01453168785095[/C][C]-0.114531687850953[/C][/ROW]
[ROW][C]49[/C][C]6.1[/C][C]6.48368239105773[/C][C]-0.383682391057733[/C][/ROW]
[ROW][C]50[/C][C]5.8[/C][C]5.99008067201732[/C][C]-0.190080672017324[/C][/ROW]
[ROW][C]51[/C][C]6.2[/C][C]6.12590273941416[/C][C]0.0740972605858393[/C][/ROW]
[ROW][C]52[/C][C]7.1[/C][C]6.830667783696[/C][C]0.269332216303996[/C][/ROW]
[ROW][C]53[/C][C]7.7[/C][C]7.68741487874814[/C][C]0.0125851212518561[/C][/ROW]
[ROW][C]54[/C][C]7.9[/C][C]7.77575173705281[/C][C]0.124248262947193[/C][/ROW]
[ROW][C]55[/C][C]7.7[/C][C]7.86147651338579[/C][C]-0.161476513385787[/C][/ROW]
[ROW][C]56[/C][C]7.4[/C][C]7.54591336043678[/C][C]-0.145913360436783[/C][/ROW]
[ROW][C]57[/C][C]7.5[/C][C]7.08450533912851[/C][C]0.415494660871489[/C][/ROW]
[ROW][C]58[/C][C]8[/C][C]7.71536999511405[/C][C]0.28463000488595[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59518&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59518&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
17.77.9001878619497-0.200187861949702
27.57.462753726799190.0372462732008101
37.67.462412627407470.137587372592530
47.87.80984619568952-0.00984619568951574
57.87.97671334402716-0.176713344027157
67.87.71217966682870.0878203331713034
77.57.74131288098777-0.241312880987771
87.57.44978867609620.0502113239038022
97.17.32456152815164-0.224561528151636
107.57.40850276036060.091497239639399
117.57.84678322380768-0.346783223807684
127.67.323833659645170.276166340354834
137.77.57148086073380.128519139266197
147.77.586810930489490.11318906951051
157.97.644484445827350.255515554172651
168.17.94496591319620.155034086803795
178.28.052245851728930.147754148271065
188.27.805295436417210.394704563582787
198.28.0092606199880.190739380011994
207.97.842750759763280.0572492402367223
217.37.38554683422236-0.0855468342223598
226.97.31344963472801-0.413449634728013
236.66.86482626483112-0.264826264831124
246.76.448565553906370.251434446093634
256.96.6951942620030.204805737997003
2676.948426371104660.0515736288953413
277.17.15445136224913-0.0544513622491277
287.27.15039337958840.0496066204115997
297.17.22917525725235-0.129175257252348
306.96.811002453588260.0889975464117454
3176.836355550263980.163644449736019
326.87.00400899613929-0.204008996139287
336.46.56120272970976-0.161202729709761
346.76.603926311538220.0960736884617774
356.66.97350955672802-0.373509556728023
366.46.54191534663454-0.141915346634544
376.36.168932093663970.131067906336030
386.26.30670666642482-0.106706666424820
396.56.465971339485760.0340286605142388
406.86.695569060093660.104430939906343
416.86.86659806388865-0.0665980638886508
426.46.7575083036855-0.357508303685495
436.16.12761669692653-0.0276166969265347
445.86.1079297854284-0.307929785428403
456.15.771323332052980.328676667947025
467.26.645335437733510.554664562266489
477.37.77309128833143-0.47309128833143
486.97.01453168785095-0.114531687850953
496.16.48368239105773-0.383682391057733
505.85.99008067201732-0.190080672017324
516.26.125902739414160.0740972605858393
527.16.8306677836960.269332216303996
537.77.687414878748140.0125851212518561
547.97.775751737052810.124248262947193
557.77.86147651338579-0.161476513385787
567.47.54591336043678-0.145913360436783
577.57.084505339128510.415494660871489
5887.715369995114050.28463000488595






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.1297997842963410.2595995685926820.870200215703659
90.1226469278007800.2452938556015600.87735307219922
100.06646462795133720.1329292559026740.933535372048663
110.03752755764762170.07505511529524330.962472442352378
120.1983843588301780.3967687176603560.801615641169822
130.1654322178133650.3308644356267310.834567782186635
140.1090627973202550.2181255946405110.890937202679745
150.08076008664130580.1615201732826120.919239913358694
160.04898030345272040.09796060690544080.95101969654728
170.02855366128385370.05710732256770740.971446338716146
180.02439518401584360.04879036803168710.975604815984156
190.02358947892014980.04717895784029950.97641052107985
200.03842186845675820.07684373691351630.961578131543242
210.1162743753748060.2325487507496120.883725624625194
220.3144703258286400.6289406516572790.68552967417136
230.3001055346643920.6002110693287840.699894465335608
240.3270764909074070.6541529818148150.672923509092592
250.2986378883917910.5972757767835810.70136211160821
260.2466095993799130.4932191987598250.753390400620087
270.1901087290818510.3802174581637030.809891270918148
280.1477387761434090.2954775522868180.85226122385659
290.1196308739380080.2392617478760160.880369126061992
300.1083839251089970.2167678502179950.891616074891003
310.1174438759559040.2348877519118070.882556124044096
320.1082769103264600.2165538206529190.89172308967354
330.09591757532561210.1918351506512240.904082424674388
340.0993924765154910.1987849530309820.90060752348451
350.1056032707977050.211206541595410.894396729202295
360.07722538596390340.1544507719278070.922774614036097
370.07836522440411350.1567304488082270.921634775595886
380.05322806991588360.1064561398317670.946771930084116
390.04428729564341110.08857459128682220.955712704356589
400.04981768341883360.09963536683766710.950182316581166
410.04606357250245870.09212714500491740.953936427497541
420.0426353030113190.0852706060226380.957364696988681
430.03748911185500380.07497822371000770.962510888144996
440.0286742176069930.0573484352139860.971325782393007
450.04234082854952460.08468165709904930.957659171450475
460.9217840924045870.1564318151908260.0782159075954132
470.8759117945535180.2481764108929650.124088205446482
480.9915053768865930.01698924622681440.0084946231134072
490.9748401653736130.05031966925277310.0251598346263865
500.9378634640952710.1242730718094580.0621365359047289

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.129799784296341 & 0.259599568592682 & 0.870200215703659 \tabularnewline
9 & 0.122646927800780 & 0.245293855601560 & 0.87735307219922 \tabularnewline
10 & 0.0664646279513372 & 0.132929255902674 & 0.933535372048663 \tabularnewline
11 & 0.0375275576476217 & 0.0750551152952433 & 0.962472442352378 \tabularnewline
12 & 0.198384358830178 & 0.396768717660356 & 0.801615641169822 \tabularnewline
13 & 0.165432217813365 & 0.330864435626731 & 0.834567782186635 \tabularnewline
14 & 0.109062797320255 & 0.218125594640511 & 0.890937202679745 \tabularnewline
15 & 0.0807600866413058 & 0.161520173282612 & 0.919239913358694 \tabularnewline
16 & 0.0489803034527204 & 0.0979606069054408 & 0.95101969654728 \tabularnewline
17 & 0.0285536612838537 & 0.0571073225677074 & 0.971446338716146 \tabularnewline
18 & 0.0243951840158436 & 0.0487903680316871 & 0.975604815984156 \tabularnewline
19 & 0.0235894789201498 & 0.0471789578402995 & 0.97641052107985 \tabularnewline
20 & 0.0384218684567582 & 0.0768437369135163 & 0.961578131543242 \tabularnewline
21 & 0.116274375374806 & 0.232548750749612 & 0.883725624625194 \tabularnewline
22 & 0.314470325828640 & 0.628940651657279 & 0.68552967417136 \tabularnewline
23 & 0.300105534664392 & 0.600211069328784 & 0.699894465335608 \tabularnewline
24 & 0.327076490907407 & 0.654152981814815 & 0.672923509092592 \tabularnewline
25 & 0.298637888391791 & 0.597275776783581 & 0.70136211160821 \tabularnewline
26 & 0.246609599379913 & 0.493219198759825 & 0.753390400620087 \tabularnewline
27 & 0.190108729081851 & 0.380217458163703 & 0.809891270918148 \tabularnewline
28 & 0.147738776143409 & 0.295477552286818 & 0.85226122385659 \tabularnewline
29 & 0.119630873938008 & 0.239261747876016 & 0.880369126061992 \tabularnewline
30 & 0.108383925108997 & 0.216767850217995 & 0.891616074891003 \tabularnewline
31 & 0.117443875955904 & 0.234887751911807 & 0.882556124044096 \tabularnewline
32 & 0.108276910326460 & 0.216553820652919 & 0.89172308967354 \tabularnewline
33 & 0.0959175753256121 & 0.191835150651224 & 0.904082424674388 \tabularnewline
34 & 0.099392476515491 & 0.198784953030982 & 0.90060752348451 \tabularnewline
35 & 0.105603270797705 & 0.21120654159541 & 0.894396729202295 \tabularnewline
36 & 0.0772253859639034 & 0.154450771927807 & 0.922774614036097 \tabularnewline
37 & 0.0783652244041135 & 0.156730448808227 & 0.921634775595886 \tabularnewline
38 & 0.0532280699158836 & 0.106456139831767 & 0.946771930084116 \tabularnewline
39 & 0.0442872956434111 & 0.0885745912868222 & 0.955712704356589 \tabularnewline
40 & 0.0498176834188336 & 0.0996353668376671 & 0.950182316581166 \tabularnewline
41 & 0.0460635725024587 & 0.0921271450049174 & 0.953936427497541 \tabularnewline
42 & 0.042635303011319 & 0.085270606022638 & 0.957364696988681 \tabularnewline
43 & 0.0374891118550038 & 0.0749782237100077 & 0.962510888144996 \tabularnewline
44 & 0.028674217606993 & 0.057348435213986 & 0.971325782393007 \tabularnewline
45 & 0.0423408285495246 & 0.0846816570990493 & 0.957659171450475 \tabularnewline
46 & 0.921784092404587 & 0.156431815190826 & 0.0782159075954132 \tabularnewline
47 & 0.875911794553518 & 0.248176410892965 & 0.124088205446482 \tabularnewline
48 & 0.991505376886593 & 0.0169892462268144 & 0.0084946231134072 \tabularnewline
49 & 0.974840165373613 & 0.0503196692527731 & 0.0251598346263865 \tabularnewline
50 & 0.937863464095271 & 0.124273071809458 & 0.0621365359047289 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59518&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]8[/C][C]0.129799784296341[/C][C]0.259599568592682[/C][C]0.870200215703659[/C][/ROW]
[ROW][C]9[/C][C]0.122646927800780[/C][C]0.245293855601560[/C][C]0.87735307219922[/C][/ROW]
[ROW][C]10[/C][C]0.0664646279513372[/C][C]0.132929255902674[/C][C]0.933535372048663[/C][/ROW]
[ROW][C]11[/C][C]0.0375275576476217[/C][C]0.0750551152952433[/C][C]0.962472442352378[/C][/ROW]
[ROW][C]12[/C][C]0.198384358830178[/C][C]0.396768717660356[/C][C]0.801615641169822[/C][/ROW]
[ROW][C]13[/C][C]0.165432217813365[/C][C]0.330864435626731[/C][C]0.834567782186635[/C][/ROW]
[ROW][C]14[/C][C]0.109062797320255[/C][C]0.218125594640511[/C][C]0.890937202679745[/C][/ROW]
[ROW][C]15[/C][C]0.0807600866413058[/C][C]0.161520173282612[/C][C]0.919239913358694[/C][/ROW]
[ROW][C]16[/C][C]0.0489803034527204[/C][C]0.0979606069054408[/C][C]0.95101969654728[/C][/ROW]
[ROW][C]17[/C][C]0.0285536612838537[/C][C]0.0571073225677074[/C][C]0.971446338716146[/C][/ROW]
[ROW][C]18[/C][C]0.0243951840158436[/C][C]0.0487903680316871[/C][C]0.975604815984156[/C][/ROW]
[ROW][C]19[/C][C]0.0235894789201498[/C][C]0.0471789578402995[/C][C]0.97641052107985[/C][/ROW]
[ROW][C]20[/C][C]0.0384218684567582[/C][C]0.0768437369135163[/C][C]0.961578131543242[/C][/ROW]
[ROW][C]21[/C][C]0.116274375374806[/C][C]0.232548750749612[/C][C]0.883725624625194[/C][/ROW]
[ROW][C]22[/C][C]0.314470325828640[/C][C]0.628940651657279[/C][C]0.68552967417136[/C][/ROW]
[ROW][C]23[/C][C]0.300105534664392[/C][C]0.600211069328784[/C][C]0.699894465335608[/C][/ROW]
[ROW][C]24[/C][C]0.327076490907407[/C][C]0.654152981814815[/C][C]0.672923509092592[/C][/ROW]
[ROW][C]25[/C][C]0.298637888391791[/C][C]0.597275776783581[/C][C]0.70136211160821[/C][/ROW]
[ROW][C]26[/C][C]0.246609599379913[/C][C]0.493219198759825[/C][C]0.753390400620087[/C][/ROW]
[ROW][C]27[/C][C]0.190108729081851[/C][C]0.380217458163703[/C][C]0.809891270918148[/C][/ROW]
[ROW][C]28[/C][C]0.147738776143409[/C][C]0.295477552286818[/C][C]0.85226122385659[/C][/ROW]
[ROW][C]29[/C][C]0.119630873938008[/C][C]0.239261747876016[/C][C]0.880369126061992[/C][/ROW]
[ROW][C]30[/C][C]0.108383925108997[/C][C]0.216767850217995[/C][C]0.891616074891003[/C][/ROW]
[ROW][C]31[/C][C]0.117443875955904[/C][C]0.234887751911807[/C][C]0.882556124044096[/C][/ROW]
[ROW][C]32[/C][C]0.108276910326460[/C][C]0.216553820652919[/C][C]0.89172308967354[/C][/ROW]
[ROW][C]33[/C][C]0.0959175753256121[/C][C]0.191835150651224[/C][C]0.904082424674388[/C][/ROW]
[ROW][C]34[/C][C]0.099392476515491[/C][C]0.198784953030982[/C][C]0.90060752348451[/C][/ROW]
[ROW][C]35[/C][C]0.105603270797705[/C][C]0.21120654159541[/C][C]0.894396729202295[/C][/ROW]
[ROW][C]36[/C][C]0.0772253859639034[/C][C]0.154450771927807[/C][C]0.922774614036097[/C][/ROW]
[ROW][C]37[/C][C]0.0783652244041135[/C][C]0.156730448808227[/C][C]0.921634775595886[/C][/ROW]
[ROW][C]38[/C][C]0.0532280699158836[/C][C]0.106456139831767[/C][C]0.946771930084116[/C][/ROW]
[ROW][C]39[/C][C]0.0442872956434111[/C][C]0.0885745912868222[/C][C]0.955712704356589[/C][/ROW]
[ROW][C]40[/C][C]0.0498176834188336[/C][C]0.0996353668376671[/C][C]0.950182316581166[/C][/ROW]
[ROW][C]41[/C][C]0.0460635725024587[/C][C]0.0921271450049174[/C][C]0.953936427497541[/C][/ROW]
[ROW][C]42[/C][C]0.042635303011319[/C][C]0.085270606022638[/C][C]0.957364696988681[/C][/ROW]
[ROW][C]43[/C][C]0.0374891118550038[/C][C]0.0749782237100077[/C][C]0.962510888144996[/C][/ROW]
[ROW][C]44[/C][C]0.028674217606993[/C][C]0.057348435213986[/C][C]0.971325782393007[/C][/ROW]
[ROW][C]45[/C][C]0.0423408285495246[/C][C]0.0846816570990493[/C][C]0.957659171450475[/C][/ROW]
[ROW][C]46[/C][C]0.921784092404587[/C][C]0.156431815190826[/C][C]0.0782159075954132[/C][/ROW]
[ROW][C]47[/C][C]0.875911794553518[/C][C]0.248176410892965[/C][C]0.124088205446482[/C][/ROW]
[ROW][C]48[/C][C]0.991505376886593[/C][C]0.0169892462268144[/C][C]0.0084946231134072[/C][/ROW]
[ROW][C]49[/C][C]0.974840165373613[/C][C]0.0503196692527731[/C][C]0.0251598346263865[/C][/ROW]
[ROW][C]50[/C][C]0.937863464095271[/C][C]0.124273071809458[/C][C]0.0621365359047289[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59518&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59518&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
80.1297997842963410.2595995685926820.870200215703659
90.1226469278007800.2452938556015600.87735307219922
100.06646462795133720.1329292559026740.933535372048663
110.03752755764762170.07505511529524330.962472442352378
120.1983843588301780.3967687176603560.801615641169822
130.1654322178133650.3308644356267310.834567782186635
140.1090627973202550.2181255946405110.890937202679745
150.08076008664130580.1615201732826120.919239913358694
160.04898030345272040.09796060690544080.95101969654728
170.02855366128385370.05710732256770740.971446338716146
180.02439518401584360.04879036803168710.975604815984156
190.02358947892014980.04717895784029950.97641052107985
200.03842186845675820.07684373691351630.961578131543242
210.1162743753748060.2325487507496120.883725624625194
220.3144703258286400.6289406516572790.68552967417136
230.3001055346643920.6002110693287840.699894465335608
240.3270764909074070.6541529818148150.672923509092592
250.2986378883917910.5972757767835810.70136211160821
260.2466095993799130.4932191987598250.753390400620087
270.1901087290818510.3802174581637030.809891270918148
280.1477387761434090.2954775522868180.85226122385659
290.1196308739380080.2392617478760160.880369126061992
300.1083839251089970.2167678502179950.891616074891003
310.1174438759559040.2348877519118070.882556124044096
320.1082769103264600.2165538206529190.89172308967354
330.09591757532561210.1918351506512240.904082424674388
340.0993924765154910.1987849530309820.90060752348451
350.1056032707977050.211206541595410.894396729202295
360.07722538596390340.1544507719278070.922774614036097
370.07836522440411350.1567304488082270.921634775595886
380.05322806991588360.1064561398317670.946771930084116
390.04428729564341110.08857459128682220.955712704356589
400.04981768341883360.09963536683766710.950182316581166
410.04606357250245870.09212714500491740.953936427497541
420.0426353030113190.0852706060226380.957364696988681
430.03748911185500380.07497822371000770.962510888144996
440.0286742176069930.0573484352139860.971325782393007
450.04234082854952460.08468165709904930.957659171450475
460.9217840924045870.1564318151908260.0782159075954132
470.8759117945535180.2481764108929650.124088205446482
480.9915053768865930.01698924622681440.0084946231134072
490.9748401653736130.05031966925277310.0251598346263865
500.9378634640952710.1242730718094580.0621365359047289






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.0697674418604651NOK
10% type I error level150.348837209302326NOK

\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 & 0.0697674418604651 & NOK \tabularnewline
10% type I error level & 15 & 0.348837209302326 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59518&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]0.0697674418604651[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]15[/C][C]0.348837209302326[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59518&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59518&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 level30.0697674418604651NOK
10% type I error level150.348837209302326NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 9
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Figure 10
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Input Parameters & R Code

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