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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 07:48:20 -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/20/t1258728627x2nxpl5jd3jut98.htm/, Retrieved Fri, 29 Mar 2024 06:03:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58229, Retrieved Fri, 29 Mar 2024 06:03:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact107
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] [] [2009-11-20 14:48:20] [3e9f70e60513fc8919624add68d96eca] [Current]
-             [Multiple Regression] [] [2009-11-20 16:39:56] [4d62210f0915d3a20cbf115865da7cd4]
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Dataseries X:
8	5560
8.1	3922
7.7	3759
7.5	4138
7.6	4634
7.8	3996
7.8	4308
7.8	4143
7.5	4429
7.5	5219
7.1	4929
7.5	5755
7.5	5592
7.6	4163
7.7	4962
7.7	5208
7.9	4755
8.1	4491
8.2	5732
8.2	5731
8.2	5040
7.9	6102
7.3	4904
6.9	5369
6.7	5578
6.7	4619
6.9	4731
7	5011
7.1	5299
7.2	4146
7.1	4625
6.9	4736
7	4219
6.8	5116
6.4	4205
6.7	4121
6.6	5103
6.4	4300
6.3	4578
6.2	3809
6.5	5526
6.8	4247
6.8	3830
6.4	4394
6.1	4826
5.8	4409
6.1	4569
7.2	4106
7.3	4794
6.9	3914
6.1	3793
5.8	4405
6.2	4022
7.1	4100
7.7	4788
7.9	3163
7.7	3585
7.4	3903
7.5	4178
8	3863
8.1	4187




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58229&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]6 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=58229&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58229&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 6.57206060903754 + 0.000134474457599655X[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  6.57206060903754 +  0.000134474457599655X[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58229&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  6.57206060903754 +  0.000134474457599655X[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58229&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58229&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 6.57206060903754 + 0.000134474457599655X[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.572060609037540.62399310.532300
X0.0001344744575996550.0001350.99720.3227560.161378

\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) & 6.57206060903754 & 0.623993 & 10.5323 & 0 & 0 \tabularnewline
X & 0.000134474457599655 & 0.000135 & 0.9972 & 0.322756 & 0.161378 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58229&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]6.57206060903754[/C][C]0.623993[/C][C]10.5323[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]0.000134474457599655[/C][C]0.000135[/C][C]0.9972[/C][C]0.322756[/C][C]0.161378[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58229&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58229&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.572060609037540.62399310.532300
X0.0001344744575996550.0001350.99720.3227560.161378







Multiple Linear Regression - Regression Statistics
Multiple R0.128739653614468
R-squared0.0165738984127731
Adjusted R-squared-9.43405971798317e-05
F-TEST (value)0.994340098127727
F-TEST (DF numerator)1
F-TEST (DF denominator)59
p-value0.322756139750018
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.661619789894276
Sum Squared Residuals25.8267040364050

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.128739653614468 \tabularnewline
R-squared & 0.0165738984127731 \tabularnewline
Adjusted R-squared & -9.43405971798317e-05 \tabularnewline
F-TEST (value) & 0.994340098127727 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 59 \tabularnewline
p-value & 0.322756139750018 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.661619789894276 \tabularnewline
Sum Squared Residuals & 25.8267040364050 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58229&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.128739653614468[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0165738984127731[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-9.43405971798317e-05[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.994340098127727[/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]0.322756139750018[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.661619789894276[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]25.8267040364050[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58229&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58229&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.128739653614468
R-squared0.0165738984127731
Adjusted R-squared-9.43405971798317e-05
F-TEST (value)0.994340098127727
F-TEST (DF numerator)1
F-TEST (DF denominator)59
p-value0.322756139750018
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.661619789894276
Sum Squared Residuals25.8267040364050







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
187.319738593291610.680261406708391
28.17.099469431743391.00053056825661
37.77.077550095154650.622449904845355
47.57.128515914584910.371484085415085
57.67.195215245554340.404784754445656
67.87.109420541605760.690579458394236
77.87.151376572376860.648623427623144
87.87.129188286872910.670811713127087
97.57.167647981746410.332352018253586
107.57.273882803250140.226117196749858
117.17.23488521054624-0.134885210546242
127.57.345961112523560.154038887476443
137.57.324041775934810.175958224065187
147.67.131877776024910.468122223975094
157.77.239322867647030.46067713235297
167.77.272403584216550.427596415783455
177.97.21148665492390.688513345076099
188.17.175985398117590.924014601882407
198.27.342868199998770.857131800001234
208.27.342733725541160.857266274458834
218.27.24981187533980.950188124660196
227.97.392623749310640.507376250689363
237.37.231523349106250.0684766508937493
246.97.29405397189009-0.39405397189009
256.77.32215913352842-0.622159133528418
266.77.19319812869035-0.493198128690349
276.97.20825926794151-0.30825926794151
2877.24591211606941-0.245912116069414
297.17.28464075985811-0.184640759858115
307.27.129591710245710.0704082897542881
317.17.19400497543595-0.0940049754359471
326.97.20893164022951-0.308931640229508
3377.13940834565049-0.139408345650487
346.87.26003193411738-0.460031934117378
356.47.13752570324409-0.737525703244091
366.77.12622984880572-0.426229848805720
376.67.25828376616858-0.658283766168582
386.47.15030077671606-0.750300776716058
396.37.18768467592876-0.887684675928763
406.27.08427381803463-0.884273818034628
416.57.31516646173324-0.815166461733236
426.87.14317363046328-0.343173630463277
436.87.08709778164422-0.287097781644221
446.47.16294137573043-0.762941375730426
456.17.22103434141348-1.12103434141348
465.87.16495849259442-1.36495849259442
476.17.18647440581037-1.08647440581037
487.27.124212731941730.0757872680582743
497.37.216731158770290.0832688412297114
506.97.09839363608259-0.198393636082592
516.17.08212222671303-0.982122226713034
525.87.16442059476402-1.36442059476402
536.27.11291687750335-0.912916877503355
547.17.12340588519613-0.0234058851961283
557.77.215924312024690.48407568797531
567.96.997403318425250.90259668157475
577.77.05415153953230.645848460467695
587.47.0969144170490.303085582951005
597.57.13389489288890.366105107111099
6087.091535438745010.90846456125499
618.17.13510516300730.964894836992702

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 8 & 7.31973859329161 & 0.680261406708391 \tabularnewline
2 & 8.1 & 7.09946943174339 & 1.00053056825661 \tabularnewline
3 & 7.7 & 7.07755009515465 & 0.622449904845355 \tabularnewline
4 & 7.5 & 7.12851591458491 & 0.371484085415085 \tabularnewline
5 & 7.6 & 7.19521524555434 & 0.404784754445656 \tabularnewline
6 & 7.8 & 7.10942054160576 & 0.690579458394236 \tabularnewline
7 & 7.8 & 7.15137657237686 & 0.648623427623144 \tabularnewline
8 & 7.8 & 7.12918828687291 & 0.670811713127087 \tabularnewline
9 & 7.5 & 7.16764798174641 & 0.332352018253586 \tabularnewline
10 & 7.5 & 7.27388280325014 & 0.226117196749858 \tabularnewline
11 & 7.1 & 7.23488521054624 & -0.134885210546242 \tabularnewline
12 & 7.5 & 7.34596111252356 & 0.154038887476443 \tabularnewline
13 & 7.5 & 7.32404177593481 & 0.175958224065187 \tabularnewline
14 & 7.6 & 7.13187777602491 & 0.468122223975094 \tabularnewline
15 & 7.7 & 7.23932286764703 & 0.46067713235297 \tabularnewline
16 & 7.7 & 7.27240358421655 & 0.427596415783455 \tabularnewline
17 & 7.9 & 7.2114866549239 & 0.688513345076099 \tabularnewline
18 & 8.1 & 7.17598539811759 & 0.924014601882407 \tabularnewline
19 & 8.2 & 7.34286819999877 & 0.857131800001234 \tabularnewline
20 & 8.2 & 7.34273372554116 & 0.857266274458834 \tabularnewline
21 & 8.2 & 7.2498118753398 & 0.950188124660196 \tabularnewline
22 & 7.9 & 7.39262374931064 & 0.507376250689363 \tabularnewline
23 & 7.3 & 7.23152334910625 & 0.0684766508937493 \tabularnewline
24 & 6.9 & 7.29405397189009 & -0.39405397189009 \tabularnewline
25 & 6.7 & 7.32215913352842 & -0.622159133528418 \tabularnewline
26 & 6.7 & 7.19319812869035 & -0.493198128690349 \tabularnewline
27 & 6.9 & 7.20825926794151 & -0.30825926794151 \tabularnewline
28 & 7 & 7.24591211606941 & -0.245912116069414 \tabularnewline
29 & 7.1 & 7.28464075985811 & -0.184640759858115 \tabularnewline
30 & 7.2 & 7.12959171024571 & 0.0704082897542881 \tabularnewline
31 & 7.1 & 7.19400497543595 & -0.0940049754359471 \tabularnewline
32 & 6.9 & 7.20893164022951 & -0.308931640229508 \tabularnewline
33 & 7 & 7.13940834565049 & -0.139408345650487 \tabularnewline
34 & 6.8 & 7.26003193411738 & -0.460031934117378 \tabularnewline
35 & 6.4 & 7.13752570324409 & -0.737525703244091 \tabularnewline
36 & 6.7 & 7.12622984880572 & -0.426229848805720 \tabularnewline
37 & 6.6 & 7.25828376616858 & -0.658283766168582 \tabularnewline
38 & 6.4 & 7.15030077671606 & -0.750300776716058 \tabularnewline
39 & 6.3 & 7.18768467592876 & -0.887684675928763 \tabularnewline
40 & 6.2 & 7.08427381803463 & -0.884273818034628 \tabularnewline
41 & 6.5 & 7.31516646173324 & -0.815166461733236 \tabularnewline
42 & 6.8 & 7.14317363046328 & -0.343173630463277 \tabularnewline
43 & 6.8 & 7.08709778164422 & -0.287097781644221 \tabularnewline
44 & 6.4 & 7.16294137573043 & -0.762941375730426 \tabularnewline
45 & 6.1 & 7.22103434141348 & -1.12103434141348 \tabularnewline
46 & 5.8 & 7.16495849259442 & -1.36495849259442 \tabularnewline
47 & 6.1 & 7.18647440581037 & -1.08647440581037 \tabularnewline
48 & 7.2 & 7.12421273194173 & 0.0757872680582743 \tabularnewline
49 & 7.3 & 7.21673115877029 & 0.0832688412297114 \tabularnewline
50 & 6.9 & 7.09839363608259 & -0.198393636082592 \tabularnewline
51 & 6.1 & 7.08212222671303 & -0.982122226713034 \tabularnewline
52 & 5.8 & 7.16442059476402 & -1.36442059476402 \tabularnewline
53 & 6.2 & 7.11291687750335 & -0.912916877503355 \tabularnewline
54 & 7.1 & 7.12340588519613 & -0.0234058851961283 \tabularnewline
55 & 7.7 & 7.21592431202469 & 0.48407568797531 \tabularnewline
56 & 7.9 & 6.99740331842525 & 0.90259668157475 \tabularnewline
57 & 7.7 & 7.0541515395323 & 0.645848460467695 \tabularnewline
58 & 7.4 & 7.096914417049 & 0.303085582951005 \tabularnewline
59 & 7.5 & 7.1338948928889 & 0.366105107111099 \tabularnewline
60 & 8 & 7.09153543874501 & 0.90846456125499 \tabularnewline
61 & 8.1 & 7.1351051630073 & 0.964894836992702 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58229&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]8[/C][C]7.31973859329161[/C][C]0.680261406708391[/C][/ROW]
[ROW][C]2[/C][C]8.1[/C][C]7.09946943174339[/C][C]1.00053056825661[/C][/ROW]
[ROW][C]3[/C][C]7.7[/C][C]7.07755009515465[/C][C]0.622449904845355[/C][/ROW]
[ROW][C]4[/C][C]7.5[/C][C]7.12851591458491[/C][C]0.371484085415085[/C][/ROW]
[ROW][C]5[/C][C]7.6[/C][C]7.19521524555434[/C][C]0.404784754445656[/C][/ROW]
[ROW][C]6[/C][C]7.8[/C][C]7.10942054160576[/C][C]0.690579458394236[/C][/ROW]
[ROW][C]7[/C][C]7.8[/C][C]7.15137657237686[/C][C]0.648623427623144[/C][/ROW]
[ROW][C]8[/C][C]7.8[/C][C]7.12918828687291[/C][C]0.670811713127087[/C][/ROW]
[ROW][C]9[/C][C]7.5[/C][C]7.16764798174641[/C][C]0.332352018253586[/C][/ROW]
[ROW][C]10[/C][C]7.5[/C][C]7.27388280325014[/C][C]0.226117196749858[/C][/ROW]
[ROW][C]11[/C][C]7.1[/C][C]7.23488521054624[/C][C]-0.134885210546242[/C][/ROW]
[ROW][C]12[/C][C]7.5[/C][C]7.34596111252356[/C][C]0.154038887476443[/C][/ROW]
[ROW][C]13[/C][C]7.5[/C][C]7.32404177593481[/C][C]0.175958224065187[/C][/ROW]
[ROW][C]14[/C][C]7.6[/C][C]7.13187777602491[/C][C]0.468122223975094[/C][/ROW]
[ROW][C]15[/C][C]7.7[/C][C]7.23932286764703[/C][C]0.46067713235297[/C][/ROW]
[ROW][C]16[/C][C]7.7[/C][C]7.27240358421655[/C][C]0.427596415783455[/C][/ROW]
[ROW][C]17[/C][C]7.9[/C][C]7.2114866549239[/C][C]0.688513345076099[/C][/ROW]
[ROW][C]18[/C][C]8.1[/C][C]7.17598539811759[/C][C]0.924014601882407[/C][/ROW]
[ROW][C]19[/C][C]8.2[/C][C]7.34286819999877[/C][C]0.857131800001234[/C][/ROW]
[ROW][C]20[/C][C]8.2[/C][C]7.34273372554116[/C][C]0.857266274458834[/C][/ROW]
[ROW][C]21[/C][C]8.2[/C][C]7.2498118753398[/C][C]0.950188124660196[/C][/ROW]
[ROW][C]22[/C][C]7.9[/C][C]7.39262374931064[/C][C]0.507376250689363[/C][/ROW]
[ROW][C]23[/C][C]7.3[/C][C]7.23152334910625[/C][C]0.0684766508937493[/C][/ROW]
[ROW][C]24[/C][C]6.9[/C][C]7.29405397189009[/C][C]-0.39405397189009[/C][/ROW]
[ROW][C]25[/C][C]6.7[/C][C]7.32215913352842[/C][C]-0.622159133528418[/C][/ROW]
[ROW][C]26[/C][C]6.7[/C][C]7.19319812869035[/C][C]-0.493198128690349[/C][/ROW]
[ROW][C]27[/C][C]6.9[/C][C]7.20825926794151[/C][C]-0.30825926794151[/C][/ROW]
[ROW][C]28[/C][C]7[/C][C]7.24591211606941[/C][C]-0.245912116069414[/C][/ROW]
[ROW][C]29[/C][C]7.1[/C][C]7.28464075985811[/C][C]-0.184640759858115[/C][/ROW]
[ROW][C]30[/C][C]7.2[/C][C]7.12959171024571[/C][C]0.0704082897542881[/C][/ROW]
[ROW][C]31[/C][C]7.1[/C][C]7.19400497543595[/C][C]-0.0940049754359471[/C][/ROW]
[ROW][C]32[/C][C]6.9[/C][C]7.20893164022951[/C][C]-0.308931640229508[/C][/ROW]
[ROW][C]33[/C][C]7[/C][C]7.13940834565049[/C][C]-0.139408345650487[/C][/ROW]
[ROW][C]34[/C][C]6.8[/C][C]7.26003193411738[/C][C]-0.460031934117378[/C][/ROW]
[ROW][C]35[/C][C]6.4[/C][C]7.13752570324409[/C][C]-0.737525703244091[/C][/ROW]
[ROW][C]36[/C][C]6.7[/C][C]7.12622984880572[/C][C]-0.426229848805720[/C][/ROW]
[ROW][C]37[/C][C]6.6[/C][C]7.25828376616858[/C][C]-0.658283766168582[/C][/ROW]
[ROW][C]38[/C][C]6.4[/C][C]7.15030077671606[/C][C]-0.750300776716058[/C][/ROW]
[ROW][C]39[/C][C]6.3[/C][C]7.18768467592876[/C][C]-0.887684675928763[/C][/ROW]
[ROW][C]40[/C][C]6.2[/C][C]7.08427381803463[/C][C]-0.884273818034628[/C][/ROW]
[ROW][C]41[/C][C]6.5[/C][C]7.31516646173324[/C][C]-0.815166461733236[/C][/ROW]
[ROW][C]42[/C][C]6.8[/C][C]7.14317363046328[/C][C]-0.343173630463277[/C][/ROW]
[ROW][C]43[/C][C]6.8[/C][C]7.08709778164422[/C][C]-0.287097781644221[/C][/ROW]
[ROW][C]44[/C][C]6.4[/C][C]7.16294137573043[/C][C]-0.762941375730426[/C][/ROW]
[ROW][C]45[/C][C]6.1[/C][C]7.22103434141348[/C][C]-1.12103434141348[/C][/ROW]
[ROW][C]46[/C][C]5.8[/C][C]7.16495849259442[/C][C]-1.36495849259442[/C][/ROW]
[ROW][C]47[/C][C]6.1[/C][C]7.18647440581037[/C][C]-1.08647440581037[/C][/ROW]
[ROW][C]48[/C][C]7.2[/C][C]7.12421273194173[/C][C]0.0757872680582743[/C][/ROW]
[ROW][C]49[/C][C]7.3[/C][C]7.21673115877029[/C][C]0.0832688412297114[/C][/ROW]
[ROW][C]50[/C][C]6.9[/C][C]7.09839363608259[/C][C]-0.198393636082592[/C][/ROW]
[ROW][C]51[/C][C]6.1[/C][C]7.08212222671303[/C][C]-0.982122226713034[/C][/ROW]
[ROW][C]52[/C][C]5.8[/C][C]7.16442059476402[/C][C]-1.36442059476402[/C][/ROW]
[ROW][C]53[/C][C]6.2[/C][C]7.11291687750335[/C][C]-0.912916877503355[/C][/ROW]
[ROW][C]54[/C][C]7.1[/C][C]7.12340588519613[/C][C]-0.0234058851961283[/C][/ROW]
[ROW][C]55[/C][C]7.7[/C][C]7.21592431202469[/C][C]0.48407568797531[/C][/ROW]
[ROW][C]56[/C][C]7.9[/C][C]6.99740331842525[/C][C]0.90259668157475[/C][/ROW]
[ROW][C]57[/C][C]7.7[/C][C]7.0541515395323[/C][C]0.645848460467695[/C][/ROW]
[ROW][C]58[/C][C]7.4[/C][C]7.096914417049[/C][C]0.303085582951005[/C][/ROW]
[ROW][C]59[/C][C]7.5[/C][C]7.1338948928889[/C][C]0.366105107111099[/C][/ROW]
[ROW][C]60[/C][C]8[/C][C]7.09153543874501[/C][C]0.90846456125499[/C][/ROW]
[ROW][C]61[/C][C]8.1[/C][C]7.1351051630073[/C][C]0.964894836992702[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58229&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58229&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
187.319738593291610.680261406708391
28.17.099469431743391.00053056825661
37.77.077550095154650.622449904845355
47.57.128515914584910.371484085415085
57.67.195215245554340.404784754445656
67.87.109420541605760.690579458394236
77.87.151376572376860.648623427623144
87.87.129188286872910.670811713127087
97.57.167647981746410.332352018253586
107.57.273882803250140.226117196749858
117.17.23488521054624-0.134885210546242
127.57.345961112523560.154038887476443
137.57.324041775934810.175958224065187
147.67.131877776024910.468122223975094
157.77.239322867647030.46067713235297
167.77.272403584216550.427596415783455
177.97.21148665492390.688513345076099
188.17.175985398117590.924014601882407
198.27.342868199998770.857131800001234
208.27.342733725541160.857266274458834
218.27.24981187533980.950188124660196
227.97.392623749310640.507376250689363
237.37.231523349106250.0684766508937493
246.97.29405397189009-0.39405397189009
256.77.32215913352842-0.622159133528418
266.77.19319812869035-0.493198128690349
276.97.20825926794151-0.30825926794151
2877.24591211606941-0.245912116069414
297.17.28464075985811-0.184640759858115
307.27.129591710245710.0704082897542881
317.17.19400497543595-0.0940049754359471
326.97.20893164022951-0.308931640229508
3377.13940834565049-0.139408345650487
346.87.26003193411738-0.460031934117378
356.47.13752570324409-0.737525703244091
366.77.12622984880572-0.426229848805720
376.67.25828376616858-0.658283766168582
386.47.15030077671606-0.750300776716058
396.37.18768467592876-0.887684675928763
406.27.08427381803463-0.884273818034628
416.57.31516646173324-0.815166461733236
426.87.14317363046328-0.343173630463277
436.87.08709778164422-0.287097781644221
446.47.16294137573043-0.762941375730426
456.17.22103434141348-1.12103434141348
465.87.16495849259442-1.36495849259442
476.17.18647440581037-1.08647440581037
487.27.124212731941730.0757872680582743
497.37.216731158770290.0832688412297114
506.97.09839363608259-0.198393636082592
516.17.08212222671303-0.982122226713034
525.87.16442059476402-1.36442059476402
536.27.11291687750335-0.912916877503355
547.17.12340588519613-0.0234058851961283
557.77.215924312024690.48407568797531
567.96.997403318425250.90259668157475
577.77.05415153953230.645848460467695
587.47.0969144170490.303085582951005
597.57.13389489288890.366105107111099
6087.091535438745010.90846456125499
618.17.13510516300730.964894836992702







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.09929855637814040.1985971127562810.90070144362186
60.03657416444463740.07314832888927490.963425835555363
70.01228895158336700.02457790316673400.987711048416633
80.003937798094244590.007875596188489170.996062201905755
90.002628570789604890.005257141579209780.997371429210395
100.001516607335279820.003033214670559640.99848339266472
110.004695255500880940.009390511001761880.99530474449912
120.001803246706906360.003606493413812720.998196753293094
130.0006630539664298460.001326107932859690.99933694603357
140.0002691590699567440.0005383181399134880.999730840930043
150.0001048250368403640.0002096500736807270.99989517496316
164.0829271095561e-058.1658542191122e-050.999959170728904
172.73893461295734e-055.47786922591467e-050.99997261065387
184.78795769907238e-059.57591539814476e-050.99995212042301
190.0001869123914364960.0003738247828729920.999813087608564
200.0004070784879625480.0008141569759250970.999592921512037
210.0009045482945115860.001809096589023170.999095451705488
220.0009749429025528180.001949885805105640.999025057097447
230.00126982586169240.00253965172338480.998730174138308
240.00584139722840010.01168279445680020.9941586027716
250.02257171112429250.0451434222485850.977428288875708
260.05002027176335970.1000405435267190.94997972823664
270.05894444238789680.1178888847757940.941055557612103
280.05914122395982880.1182824479196580.940858776040171
290.05753206080490050.1150641216098010.9424679391951
300.04634683456203480.09269366912406960.953653165437965
310.04039402424854570.08078804849709150.959605975751454
320.03982348293072660.07964696586145310.960176517069273
330.03324371944413750.0664874388882750.966756280555862
340.03538368399386410.07076736798772820.964616316006136
350.05776422596297070.1155284519259410.94223577403703
360.0538648374075430.1077296748150860.946135162592457
370.05682543811448590.1136508762289720.943174561885514
380.0677319390827020.1354638781654040.932268060917298
390.08305368111782210.1661073622356440.916946318882178
400.1240148205960550.248029641192110.875985179403945
410.1278921864988270.2557843729976550.872107813501173
420.09629101897080130.1925820379416030.9037089810292
430.07377746025880790.1475549205176160.926222539741192
440.06822836136178160.1364567227235630.931771638638218
450.08045243786364910.1609048757272980.91954756213635
460.167319286625380.334638573250760.83268071337462
470.2184281119519020.4368562239038050.781571888048098
480.1573749990541480.3147499981082960.842625000945852
490.1139874610304710.2279749220609430.886012538969529
500.07949786150515450.1589957230103090.920502138494846
510.1653518141751230.3307036283502460.834648185824877
520.5027420917101850.994515816579630.497257908289815
530.9346803726434830.1306392547130330.0653196273565167
540.9654158154928450.06916836901430990.0345841845071549
550.915829350294280.168341299411440.08417064970572
560.8350923884166040.3298152231667920.164907611583396

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.0992985563781404 & 0.198597112756281 & 0.90070144362186 \tabularnewline
6 & 0.0365741644446374 & 0.0731483288892749 & 0.963425835555363 \tabularnewline
7 & 0.0122889515833670 & 0.0245779031667340 & 0.987711048416633 \tabularnewline
8 & 0.00393779809424459 & 0.00787559618848917 & 0.996062201905755 \tabularnewline
9 & 0.00262857078960489 & 0.00525714157920978 & 0.997371429210395 \tabularnewline
10 & 0.00151660733527982 & 0.00303321467055964 & 0.99848339266472 \tabularnewline
11 & 0.00469525550088094 & 0.00939051100176188 & 0.99530474449912 \tabularnewline
12 & 0.00180324670690636 & 0.00360649341381272 & 0.998196753293094 \tabularnewline
13 & 0.000663053966429846 & 0.00132610793285969 & 0.99933694603357 \tabularnewline
14 & 0.000269159069956744 & 0.000538318139913488 & 0.999730840930043 \tabularnewline
15 & 0.000104825036840364 & 0.000209650073680727 & 0.99989517496316 \tabularnewline
16 & 4.0829271095561e-05 & 8.1658542191122e-05 & 0.999959170728904 \tabularnewline
17 & 2.73893461295734e-05 & 5.47786922591467e-05 & 0.99997261065387 \tabularnewline
18 & 4.78795769907238e-05 & 9.57591539814476e-05 & 0.99995212042301 \tabularnewline
19 & 0.000186912391436496 & 0.000373824782872992 & 0.999813087608564 \tabularnewline
20 & 0.000407078487962548 & 0.000814156975925097 & 0.999592921512037 \tabularnewline
21 & 0.000904548294511586 & 0.00180909658902317 & 0.999095451705488 \tabularnewline
22 & 0.000974942902552818 & 0.00194988580510564 & 0.999025057097447 \tabularnewline
23 & 0.0012698258616924 & 0.0025396517233848 & 0.998730174138308 \tabularnewline
24 & 0.0058413972284001 & 0.0116827944568002 & 0.9941586027716 \tabularnewline
25 & 0.0225717111242925 & 0.045143422248585 & 0.977428288875708 \tabularnewline
26 & 0.0500202717633597 & 0.100040543526719 & 0.94997972823664 \tabularnewline
27 & 0.0589444423878968 & 0.117888884775794 & 0.941055557612103 \tabularnewline
28 & 0.0591412239598288 & 0.118282447919658 & 0.940858776040171 \tabularnewline
29 & 0.0575320608049005 & 0.115064121609801 & 0.9424679391951 \tabularnewline
30 & 0.0463468345620348 & 0.0926936691240696 & 0.953653165437965 \tabularnewline
31 & 0.0403940242485457 & 0.0807880484970915 & 0.959605975751454 \tabularnewline
32 & 0.0398234829307266 & 0.0796469658614531 & 0.960176517069273 \tabularnewline
33 & 0.0332437194441375 & 0.066487438888275 & 0.966756280555862 \tabularnewline
34 & 0.0353836839938641 & 0.0707673679877282 & 0.964616316006136 \tabularnewline
35 & 0.0577642259629707 & 0.115528451925941 & 0.94223577403703 \tabularnewline
36 & 0.053864837407543 & 0.107729674815086 & 0.946135162592457 \tabularnewline
37 & 0.0568254381144859 & 0.113650876228972 & 0.943174561885514 \tabularnewline
38 & 0.067731939082702 & 0.135463878165404 & 0.932268060917298 \tabularnewline
39 & 0.0830536811178221 & 0.166107362235644 & 0.916946318882178 \tabularnewline
40 & 0.124014820596055 & 0.24802964119211 & 0.875985179403945 \tabularnewline
41 & 0.127892186498827 & 0.255784372997655 & 0.872107813501173 \tabularnewline
42 & 0.0962910189708013 & 0.192582037941603 & 0.9037089810292 \tabularnewline
43 & 0.0737774602588079 & 0.147554920517616 & 0.926222539741192 \tabularnewline
44 & 0.0682283613617816 & 0.136456722723563 & 0.931771638638218 \tabularnewline
45 & 0.0804524378636491 & 0.160904875727298 & 0.91954756213635 \tabularnewline
46 & 0.16731928662538 & 0.33463857325076 & 0.83268071337462 \tabularnewline
47 & 0.218428111951902 & 0.436856223903805 & 0.781571888048098 \tabularnewline
48 & 0.157374999054148 & 0.314749998108296 & 0.842625000945852 \tabularnewline
49 & 0.113987461030471 & 0.227974922060943 & 0.886012538969529 \tabularnewline
50 & 0.0794978615051545 & 0.158995723010309 & 0.920502138494846 \tabularnewline
51 & 0.165351814175123 & 0.330703628350246 & 0.834648185824877 \tabularnewline
52 & 0.502742091710185 & 0.99451581657963 & 0.497257908289815 \tabularnewline
53 & 0.934680372643483 & 0.130639254713033 & 0.0653196273565167 \tabularnewline
54 & 0.965415815492845 & 0.0691683690143099 & 0.0345841845071549 \tabularnewline
55 & 0.91582935029428 & 0.16834129941144 & 0.08417064970572 \tabularnewline
56 & 0.835092388416604 & 0.329815223166792 & 0.164907611583396 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58229&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]0.0992985563781404[/C][C]0.198597112756281[/C][C]0.90070144362186[/C][/ROW]
[ROW][C]6[/C][C]0.0365741644446374[/C][C]0.0731483288892749[/C][C]0.963425835555363[/C][/ROW]
[ROW][C]7[/C][C]0.0122889515833670[/C][C]0.0245779031667340[/C][C]0.987711048416633[/C][/ROW]
[ROW][C]8[/C][C]0.00393779809424459[/C][C]0.00787559618848917[/C][C]0.996062201905755[/C][/ROW]
[ROW][C]9[/C][C]0.00262857078960489[/C][C]0.00525714157920978[/C][C]0.997371429210395[/C][/ROW]
[ROW][C]10[/C][C]0.00151660733527982[/C][C]0.00303321467055964[/C][C]0.99848339266472[/C][/ROW]
[ROW][C]11[/C][C]0.00469525550088094[/C][C]0.00939051100176188[/C][C]0.99530474449912[/C][/ROW]
[ROW][C]12[/C][C]0.00180324670690636[/C][C]0.00360649341381272[/C][C]0.998196753293094[/C][/ROW]
[ROW][C]13[/C][C]0.000663053966429846[/C][C]0.00132610793285969[/C][C]0.99933694603357[/C][/ROW]
[ROW][C]14[/C][C]0.000269159069956744[/C][C]0.000538318139913488[/C][C]0.999730840930043[/C][/ROW]
[ROW][C]15[/C][C]0.000104825036840364[/C][C]0.000209650073680727[/C][C]0.99989517496316[/C][/ROW]
[ROW][C]16[/C][C]4.0829271095561e-05[/C][C]8.1658542191122e-05[/C][C]0.999959170728904[/C][/ROW]
[ROW][C]17[/C][C]2.73893461295734e-05[/C][C]5.47786922591467e-05[/C][C]0.99997261065387[/C][/ROW]
[ROW][C]18[/C][C]4.78795769907238e-05[/C][C]9.57591539814476e-05[/C][C]0.99995212042301[/C][/ROW]
[ROW][C]19[/C][C]0.000186912391436496[/C][C]0.000373824782872992[/C][C]0.999813087608564[/C][/ROW]
[ROW][C]20[/C][C]0.000407078487962548[/C][C]0.000814156975925097[/C][C]0.999592921512037[/C][/ROW]
[ROW][C]21[/C][C]0.000904548294511586[/C][C]0.00180909658902317[/C][C]0.999095451705488[/C][/ROW]
[ROW][C]22[/C][C]0.000974942902552818[/C][C]0.00194988580510564[/C][C]0.999025057097447[/C][/ROW]
[ROW][C]23[/C][C]0.0012698258616924[/C][C]0.0025396517233848[/C][C]0.998730174138308[/C][/ROW]
[ROW][C]24[/C][C]0.0058413972284001[/C][C]0.0116827944568002[/C][C]0.9941586027716[/C][/ROW]
[ROW][C]25[/C][C]0.0225717111242925[/C][C]0.045143422248585[/C][C]0.977428288875708[/C][/ROW]
[ROW][C]26[/C][C]0.0500202717633597[/C][C]0.100040543526719[/C][C]0.94997972823664[/C][/ROW]
[ROW][C]27[/C][C]0.0589444423878968[/C][C]0.117888884775794[/C][C]0.941055557612103[/C][/ROW]
[ROW][C]28[/C][C]0.0591412239598288[/C][C]0.118282447919658[/C][C]0.940858776040171[/C][/ROW]
[ROW][C]29[/C][C]0.0575320608049005[/C][C]0.115064121609801[/C][C]0.9424679391951[/C][/ROW]
[ROW][C]30[/C][C]0.0463468345620348[/C][C]0.0926936691240696[/C][C]0.953653165437965[/C][/ROW]
[ROW][C]31[/C][C]0.0403940242485457[/C][C]0.0807880484970915[/C][C]0.959605975751454[/C][/ROW]
[ROW][C]32[/C][C]0.0398234829307266[/C][C]0.0796469658614531[/C][C]0.960176517069273[/C][/ROW]
[ROW][C]33[/C][C]0.0332437194441375[/C][C]0.066487438888275[/C][C]0.966756280555862[/C][/ROW]
[ROW][C]34[/C][C]0.0353836839938641[/C][C]0.0707673679877282[/C][C]0.964616316006136[/C][/ROW]
[ROW][C]35[/C][C]0.0577642259629707[/C][C]0.115528451925941[/C][C]0.94223577403703[/C][/ROW]
[ROW][C]36[/C][C]0.053864837407543[/C][C]0.107729674815086[/C][C]0.946135162592457[/C][/ROW]
[ROW][C]37[/C][C]0.0568254381144859[/C][C]0.113650876228972[/C][C]0.943174561885514[/C][/ROW]
[ROW][C]38[/C][C]0.067731939082702[/C][C]0.135463878165404[/C][C]0.932268060917298[/C][/ROW]
[ROW][C]39[/C][C]0.0830536811178221[/C][C]0.166107362235644[/C][C]0.916946318882178[/C][/ROW]
[ROW][C]40[/C][C]0.124014820596055[/C][C]0.24802964119211[/C][C]0.875985179403945[/C][/ROW]
[ROW][C]41[/C][C]0.127892186498827[/C][C]0.255784372997655[/C][C]0.872107813501173[/C][/ROW]
[ROW][C]42[/C][C]0.0962910189708013[/C][C]0.192582037941603[/C][C]0.9037089810292[/C][/ROW]
[ROW][C]43[/C][C]0.0737774602588079[/C][C]0.147554920517616[/C][C]0.926222539741192[/C][/ROW]
[ROW][C]44[/C][C]0.0682283613617816[/C][C]0.136456722723563[/C][C]0.931771638638218[/C][/ROW]
[ROW][C]45[/C][C]0.0804524378636491[/C][C]0.160904875727298[/C][C]0.91954756213635[/C][/ROW]
[ROW][C]46[/C][C]0.16731928662538[/C][C]0.33463857325076[/C][C]0.83268071337462[/C][/ROW]
[ROW][C]47[/C][C]0.218428111951902[/C][C]0.436856223903805[/C][C]0.781571888048098[/C][/ROW]
[ROW][C]48[/C][C]0.157374999054148[/C][C]0.314749998108296[/C][C]0.842625000945852[/C][/ROW]
[ROW][C]49[/C][C]0.113987461030471[/C][C]0.227974922060943[/C][C]0.886012538969529[/C][/ROW]
[ROW][C]50[/C][C]0.0794978615051545[/C][C]0.158995723010309[/C][C]0.920502138494846[/C][/ROW]
[ROW][C]51[/C][C]0.165351814175123[/C][C]0.330703628350246[/C][C]0.834648185824877[/C][/ROW]
[ROW][C]52[/C][C]0.502742091710185[/C][C]0.99451581657963[/C][C]0.497257908289815[/C][/ROW]
[ROW][C]53[/C][C]0.934680372643483[/C][C]0.130639254713033[/C][C]0.0653196273565167[/C][/ROW]
[ROW][C]54[/C][C]0.965415815492845[/C][C]0.0691683690143099[/C][C]0.0345841845071549[/C][/ROW]
[ROW][C]55[/C][C]0.91582935029428[/C][C]0.16834129941144[/C][C]0.08417064970572[/C][/ROW]
[ROW][C]56[/C][C]0.835092388416604[/C][C]0.329815223166792[/C][C]0.164907611583396[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58229&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58229&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.09929855637814040.1985971127562810.90070144362186
60.03657416444463740.07314832888927490.963425835555363
70.01228895158336700.02457790316673400.987711048416633
80.003937798094244590.007875596188489170.996062201905755
90.002628570789604890.005257141579209780.997371429210395
100.001516607335279820.003033214670559640.99848339266472
110.004695255500880940.009390511001761880.99530474449912
120.001803246706906360.003606493413812720.998196753293094
130.0006630539664298460.001326107932859690.99933694603357
140.0002691590699567440.0005383181399134880.999730840930043
150.0001048250368403640.0002096500736807270.99989517496316
164.0829271095561e-058.1658542191122e-050.999959170728904
172.73893461295734e-055.47786922591467e-050.99997261065387
184.78795769907238e-059.57591539814476e-050.99995212042301
190.0001869123914364960.0003738247828729920.999813087608564
200.0004070784879625480.0008141569759250970.999592921512037
210.0009045482945115860.001809096589023170.999095451705488
220.0009749429025528180.001949885805105640.999025057097447
230.00126982586169240.00253965172338480.998730174138308
240.00584139722840010.01168279445680020.9941586027716
250.02257171112429250.0451434222485850.977428288875708
260.05002027176335970.1000405435267190.94997972823664
270.05894444238789680.1178888847757940.941055557612103
280.05914122395982880.1182824479196580.940858776040171
290.05753206080490050.1150641216098010.9424679391951
300.04634683456203480.09269366912406960.953653165437965
310.04039402424854570.08078804849709150.959605975751454
320.03982348293072660.07964696586145310.960176517069273
330.03324371944413750.0664874388882750.966756280555862
340.03538368399386410.07076736798772820.964616316006136
350.05776422596297070.1155284519259410.94223577403703
360.0538648374075430.1077296748150860.946135162592457
370.05682543811448590.1136508762289720.943174561885514
380.0677319390827020.1354638781654040.932268060917298
390.08305368111782210.1661073622356440.916946318882178
400.1240148205960550.248029641192110.875985179403945
410.1278921864988270.2557843729976550.872107813501173
420.09629101897080130.1925820379416030.9037089810292
430.07377746025880790.1475549205176160.926222539741192
440.06822836136178160.1364567227235630.931771638638218
450.08045243786364910.1609048757272980.91954756213635
460.167319286625380.334638573250760.83268071337462
470.2184281119519020.4368562239038050.781571888048098
480.1573749990541480.3147499981082960.842625000945852
490.1139874610304710.2279749220609430.886012538969529
500.07949786150515450.1589957230103090.920502138494846
510.1653518141751230.3307036283502460.834648185824877
520.5027420917101850.994515816579630.497257908289815
530.9346803726434830.1306392547130330.0653196273565167
540.9654158154928450.06916836901430990.0345841845071549
550.915829350294280.168341299411440.08417064970572
560.8350923884166040.3298152231667920.164907611583396







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level160.307692307692308NOK
5% type I error level190.365384615384615NOK
10% type I error level260.5NOK

\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 & 16 & 0.307692307692308 & NOK \tabularnewline
5% type I error level & 19 & 0.365384615384615 & NOK \tabularnewline
10% type I error level & 26 & 0.5 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58229&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]16[/C][C]0.307692307692308[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]19[/C][C]0.365384615384615[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]26[/C][C]0.5[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58229&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58229&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level160.307692307692308NOK
5% type I error level190.365384615384615NOK
10% type I error level260.5NOK



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')
}