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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 computationThu, 19 Nov 2009 08:14:30 -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/19/t1258643741j23z5s3pkh494kc.htm/, Retrieved Fri, 29 Mar 2024 11:22:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57752, Retrieved Fri, 29 Mar 2024 11:22:50 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact142
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] [WS7-1] [2009-11-19 15:14:30] [30970b478e356ce7f8c2e9fca280b230] [Current]
-    D        [Multiple Regression] [WS7-1Werkloosheid] [2009-11-19 15:40:05] [a94022e7c2399c0f4d62eea578db3411]
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Dataseries X:
10.9	96.8
10	114.1
9.2	110.3
9.2	103.9
9.5	101.6
9.6	94.6
9.5	95.9
9.1	104.7
8.9	102.8
9	98.1
10.1	113.9
10.3	80.9
10.2	95.7
9.6	113.2
9.2	105.9
9.3	108.8
9.4	102.3
9.4	99
9.2	100.7
9	115.5
9	100.7
9	109.9
9.8	114.6
10	85.4
9.8	100.5
9.3	114.8
9	116.5
9	112.9
9.1	102
9.1	106
9.1	105.3
9.2	118.8
8.8	106.1
8.3	109.3
8.4	117.2
8.1	92.5
7.7	104.2
7.9	112.5
7.9	122.4
8	113.3
7.9	100
7.6	110.7
7.1	112.8
6.8	109.8
6.5	117.3
6.9	109.1
8.2	115.9
8.7	96
8.3	99.8
7.9	116.8
7.5	115.7
7.8	99.4
8.3	94.3
8.4	91
8.2	93.2
7.7	103.1
7.2	94.1
7.3	91.8
8.1	102.7
8.5	82.6




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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57752&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57752&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57752&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 10.8290392669577 -0.0203421112501333X[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  10.8290392669577 -0.0203421112501333X[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57752&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  10.8290392669577 -0.0203421112501333X[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57752&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57752&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] = + 10.8290392669577 -0.0203421112501333X[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)10.82903926695771.3577147.975900
X-0.02034211125013330.012921-1.57440.1208340.060417

\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) & 10.8290392669577 & 1.357714 & 7.9759 & 0 & 0 \tabularnewline
X & -0.0203421112501333 & 0.012921 & -1.5744 & 0.120834 & 0.060417 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57752&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]10.8290392669577[/C][C]1.357714[/C][C]7.9759[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-0.0203421112501333[/C][C]0.012921[/C][C]-1.5744[/C][C]0.120834[/C][C]0.060417[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57752&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57752&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)10.82903926695771.3577147.975900
X-0.02034211125013330.012921-1.57440.1208340.060417







Multiple Linear Regression - Regression Statistics
Multiple R0.202448207205748
R-squared0.0409852766008213
Adjusted R-squared0.0244505399904907
F-TEST (value)2.47873779708195
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0.120834348025313
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.93948083549344
Sum Squared Residuals51.1922059350481

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.202448207205748 \tabularnewline
R-squared & 0.0409852766008213 \tabularnewline
Adjusted R-squared & 0.0244505399904907 \tabularnewline
F-TEST (value) & 2.47873779708195 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 0.120834348025313 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.93948083549344 \tabularnewline
Sum Squared Residuals & 51.1922059350481 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57752&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.202448207205748[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0409852766008213[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0244505399904907[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.47873779708195[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]0.120834348025313[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.93948083549344[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]51.1922059350481[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57752&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57752&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 R0.202448207205748
R-squared0.0409852766008213
Adjusted R-squared0.0244505399904907
F-TEST (value)2.47873779708195
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0.120834348025313
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.93948083549344
Sum Squared Residuals51.1922059350481







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
110.98.85992289794482.04007710205520
2108.508004373317491.49199562668251
39.28.5853043960680.614695603932001
49.28.715493908068850.484506091931148
59.58.762280763944160.737719236055842
69.68.90467554269510.695324457304909
79.58.878230798069920.621769201930083
89.18.699220219068740.400779780931255
98.98.7378702304440.162129769556002
1098.833478153319620.166521846680376
1110.18.512072795567521.58792720443248
1210.39.183362466821921.11663753317808
1310.28.882299220319941.31770077968006
149.68.526312273442611.07368772655739
159.28.674809685568580.525190314431415
169.38.61581756294320.684182437056802
179.48.748041286069060.651958713930936
189.48.81517025319450.584829746805496
199.28.780588664069280.419411335930721
2098.47952541756730.520474582432695
2198.780588664069280.219411335930722
2298.593441240568050.406558759431948
239.88.497833317692431.30216668230758
24109.091822966196320.908177033803683
259.88.78465708631931.01534291368070
269.38.49376489544240.806235104557602
2798.459183306317170.540816693682828
2898.532414906817650.467585093182348
299.18.75414391944410.345856080555895
309.18.672775474443570.427224525556428
319.18.687014952318660.412985047681335
329.28.412396450441870.787603549558134
338.88.670741263318560.129258736681442
348.38.60564650731813-0.305646507318131
358.48.44494382844208-0.0449438284420785
368.18.94739397632037-0.847393976320371
377.78.70939127469381-1.00939127469381
387.98.5405517513177-0.640551751317705
397.98.33916484994139-0.439164849941385
4088.5242780623176-0.524278062317599
417.98.79482814194437-0.894828141944371
427.68.57716755156794-0.977167551567946
437.18.53444911794267-1.43444911794267
446.88.59547545169307-1.79547545169307
456.58.44290961731707-1.94290961731707
466.98.60971492956816-1.70971492956816
478.28.47138857306725-0.271388573067253
488.78.8761965869449-0.176196586944905
498.38.7988965641944-0.498896564194397
507.98.45308067294213-0.553080672942132
517.58.47545699531728-0.975456995317279
527.88.80703340869445-1.00703340869445
538.38.91077817607013-0.61077817607013
548.48.97790714319557-0.57790714319557
558.28.93315449844528-0.733154498445278
567.78.73176759706896-1.03176759706896
577.28.91484659832016-1.71484659832016
587.38.96163345419546-1.66163345419546
598.18.73990444156901-0.639904441569011
608.59.1487808776967-0.64878087769669

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 10.9 & 8.8599228979448 & 2.04007710205520 \tabularnewline
2 & 10 & 8.50800437331749 & 1.49199562668251 \tabularnewline
3 & 9.2 & 8.585304396068 & 0.614695603932001 \tabularnewline
4 & 9.2 & 8.71549390806885 & 0.484506091931148 \tabularnewline
5 & 9.5 & 8.76228076394416 & 0.737719236055842 \tabularnewline
6 & 9.6 & 8.9046755426951 & 0.695324457304909 \tabularnewline
7 & 9.5 & 8.87823079806992 & 0.621769201930083 \tabularnewline
8 & 9.1 & 8.69922021906874 & 0.400779780931255 \tabularnewline
9 & 8.9 & 8.737870230444 & 0.162129769556002 \tabularnewline
10 & 9 & 8.83347815331962 & 0.166521846680376 \tabularnewline
11 & 10.1 & 8.51207279556752 & 1.58792720443248 \tabularnewline
12 & 10.3 & 9.18336246682192 & 1.11663753317808 \tabularnewline
13 & 10.2 & 8.88229922031994 & 1.31770077968006 \tabularnewline
14 & 9.6 & 8.52631227344261 & 1.07368772655739 \tabularnewline
15 & 9.2 & 8.67480968556858 & 0.525190314431415 \tabularnewline
16 & 9.3 & 8.6158175629432 & 0.684182437056802 \tabularnewline
17 & 9.4 & 8.74804128606906 & 0.651958713930936 \tabularnewline
18 & 9.4 & 8.8151702531945 & 0.584829746805496 \tabularnewline
19 & 9.2 & 8.78058866406928 & 0.419411335930721 \tabularnewline
20 & 9 & 8.4795254175673 & 0.520474582432695 \tabularnewline
21 & 9 & 8.78058866406928 & 0.219411335930722 \tabularnewline
22 & 9 & 8.59344124056805 & 0.406558759431948 \tabularnewline
23 & 9.8 & 8.49783331769243 & 1.30216668230758 \tabularnewline
24 & 10 & 9.09182296619632 & 0.908177033803683 \tabularnewline
25 & 9.8 & 8.7846570863193 & 1.01534291368070 \tabularnewline
26 & 9.3 & 8.4937648954424 & 0.806235104557602 \tabularnewline
27 & 9 & 8.45918330631717 & 0.540816693682828 \tabularnewline
28 & 9 & 8.53241490681765 & 0.467585093182348 \tabularnewline
29 & 9.1 & 8.7541439194441 & 0.345856080555895 \tabularnewline
30 & 9.1 & 8.67277547444357 & 0.427224525556428 \tabularnewline
31 & 9.1 & 8.68701495231866 & 0.412985047681335 \tabularnewline
32 & 9.2 & 8.41239645044187 & 0.787603549558134 \tabularnewline
33 & 8.8 & 8.67074126331856 & 0.129258736681442 \tabularnewline
34 & 8.3 & 8.60564650731813 & -0.305646507318131 \tabularnewline
35 & 8.4 & 8.44494382844208 & -0.0449438284420785 \tabularnewline
36 & 8.1 & 8.94739397632037 & -0.847393976320371 \tabularnewline
37 & 7.7 & 8.70939127469381 & -1.00939127469381 \tabularnewline
38 & 7.9 & 8.5405517513177 & -0.640551751317705 \tabularnewline
39 & 7.9 & 8.33916484994139 & -0.439164849941385 \tabularnewline
40 & 8 & 8.5242780623176 & -0.524278062317599 \tabularnewline
41 & 7.9 & 8.79482814194437 & -0.894828141944371 \tabularnewline
42 & 7.6 & 8.57716755156794 & -0.977167551567946 \tabularnewline
43 & 7.1 & 8.53444911794267 & -1.43444911794267 \tabularnewline
44 & 6.8 & 8.59547545169307 & -1.79547545169307 \tabularnewline
45 & 6.5 & 8.44290961731707 & -1.94290961731707 \tabularnewline
46 & 6.9 & 8.60971492956816 & -1.70971492956816 \tabularnewline
47 & 8.2 & 8.47138857306725 & -0.271388573067253 \tabularnewline
48 & 8.7 & 8.8761965869449 & -0.176196586944905 \tabularnewline
49 & 8.3 & 8.7988965641944 & -0.498896564194397 \tabularnewline
50 & 7.9 & 8.45308067294213 & -0.553080672942132 \tabularnewline
51 & 7.5 & 8.47545699531728 & -0.975456995317279 \tabularnewline
52 & 7.8 & 8.80703340869445 & -1.00703340869445 \tabularnewline
53 & 8.3 & 8.91077817607013 & -0.61077817607013 \tabularnewline
54 & 8.4 & 8.97790714319557 & -0.57790714319557 \tabularnewline
55 & 8.2 & 8.93315449844528 & -0.733154498445278 \tabularnewline
56 & 7.7 & 8.73176759706896 & -1.03176759706896 \tabularnewline
57 & 7.2 & 8.91484659832016 & -1.71484659832016 \tabularnewline
58 & 7.3 & 8.96163345419546 & -1.66163345419546 \tabularnewline
59 & 8.1 & 8.73990444156901 & -0.639904441569011 \tabularnewline
60 & 8.5 & 9.1487808776967 & -0.64878087769669 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57752&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]10.9[/C][C]8.8599228979448[/C][C]2.04007710205520[/C][/ROW]
[ROW][C]2[/C][C]10[/C][C]8.50800437331749[/C][C]1.49199562668251[/C][/ROW]
[ROW][C]3[/C][C]9.2[/C][C]8.585304396068[/C][C]0.614695603932001[/C][/ROW]
[ROW][C]4[/C][C]9.2[/C][C]8.71549390806885[/C][C]0.484506091931148[/C][/ROW]
[ROW][C]5[/C][C]9.5[/C][C]8.76228076394416[/C][C]0.737719236055842[/C][/ROW]
[ROW][C]6[/C][C]9.6[/C][C]8.9046755426951[/C][C]0.695324457304909[/C][/ROW]
[ROW][C]7[/C][C]9.5[/C][C]8.87823079806992[/C][C]0.621769201930083[/C][/ROW]
[ROW][C]8[/C][C]9.1[/C][C]8.69922021906874[/C][C]0.400779780931255[/C][/ROW]
[ROW][C]9[/C][C]8.9[/C][C]8.737870230444[/C][C]0.162129769556002[/C][/ROW]
[ROW][C]10[/C][C]9[/C][C]8.83347815331962[/C][C]0.166521846680376[/C][/ROW]
[ROW][C]11[/C][C]10.1[/C][C]8.51207279556752[/C][C]1.58792720443248[/C][/ROW]
[ROW][C]12[/C][C]10.3[/C][C]9.18336246682192[/C][C]1.11663753317808[/C][/ROW]
[ROW][C]13[/C][C]10.2[/C][C]8.88229922031994[/C][C]1.31770077968006[/C][/ROW]
[ROW][C]14[/C][C]9.6[/C][C]8.52631227344261[/C][C]1.07368772655739[/C][/ROW]
[ROW][C]15[/C][C]9.2[/C][C]8.67480968556858[/C][C]0.525190314431415[/C][/ROW]
[ROW][C]16[/C][C]9.3[/C][C]8.6158175629432[/C][C]0.684182437056802[/C][/ROW]
[ROW][C]17[/C][C]9.4[/C][C]8.74804128606906[/C][C]0.651958713930936[/C][/ROW]
[ROW][C]18[/C][C]9.4[/C][C]8.8151702531945[/C][C]0.584829746805496[/C][/ROW]
[ROW][C]19[/C][C]9.2[/C][C]8.78058866406928[/C][C]0.419411335930721[/C][/ROW]
[ROW][C]20[/C][C]9[/C][C]8.4795254175673[/C][C]0.520474582432695[/C][/ROW]
[ROW][C]21[/C][C]9[/C][C]8.78058866406928[/C][C]0.219411335930722[/C][/ROW]
[ROW][C]22[/C][C]9[/C][C]8.59344124056805[/C][C]0.406558759431948[/C][/ROW]
[ROW][C]23[/C][C]9.8[/C][C]8.49783331769243[/C][C]1.30216668230758[/C][/ROW]
[ROW][C]24[/C][C]10[/C][C]9.09182296619632[/C][C]0.908177033803683[/C][/ROW]
[ROW][C]25[/C][C]9.8[/C][C]8.7846570863193[/C][C]1.01534291368070[/C][/ROW]
[ROW][C]26[/C][C]9.3[/C][C]8.4937648954424[/C][C]0.806235104557602[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]8.45918330631717[/C][C]0.540816693682828[/C][/ROW]
[ROW][C]28[/C][C]9[/C][C]8.53241490681765[/C][C]0.467585093182348[/C][/ROW]
[ROW][C]29[/C][C]9.1[/C][C]8.7541439194441[/C][C]0.345856080555895[/C][/ROW]
[ROW][C]30[/C][C]9.1[/C][C]8.67277547444357[/C][C]0.427224525556428[/C][/ROW]
[ROW][C]31[/C][C]9.1[/C][C]8.68701495231866[/C][C]0.412985047681335[/C][/ROW]
[ROW][C]32[/C][C]9.2[/C][C]8.41239645044187[/C][C]0.787603549558134[/C][/ROW]
[ROW][C]33[/C][C]8.8[/C][C]8.67074126331856[/C][C]0.129258736681442[/C][/ROW]
[ROW][C]34[/C][C]8.3[/C][C]8.60564650731813[/C][C]-0.305646507318131[/C][/ROW]
[ROW][C]35[/C][C]8.4[/C][C]8.44494382844208[/C][C]-0.0449438284420785[/C][/ROW]
[ROW][C]36[/C][C]8.1[/C][C]8.94739397632037[/C][C]-0.847393976320371[/C][/ROW]
[ROW][C]37[/C][C]7.7[/C][C]8.70939127469381[/C][C]-1.00939127469381[/C][/ROW]
[ROW][C]38[/C][C]7.9[/C][C]8.5405517513177[/C][C]-0.640551751317705[/C][/ROW]
[ROW][C]39[/C][C]7.9[/C][C]8.33916484994139[/C][C]-0.439164849941385[/C][/ROW]
[ROW][C]40[/C][C]8[/C][C]8.5242780623176[/C][C]-0.524278062317599[/C][/ROW]
[ROW][C]41[/C][C]7.9[/C][C]8.79482814194437[/C][C]-0.894828141944371[/C][/ROW]
[ROW][C]42[/C][C]7.6[/C][C]8.57716755156794[/C][C]-0.977167551567946[/C][/ROW]
[ROW][C]43[/C][C]7.1[/C][C]8.53444911794267[/C][C]-1.43444911794267[/C][/ROW]
[ROW][C]44[/C][C]6.8[/C][C]8.59547545169307[/C][C]-1.79547545169307[/C][/ROW]
[ROW][C]45[/C][C]6.5[/C][C]8.44290961731707[/C][C]-1.94290961731707[/C][/ROW]
[ROW][C]46[/C][C]6.9[/C][C]8.60971492956816[/C][C]-1.70971492956816[/C][/ROW]
[ROW][C]47[/C][C]8.2[/C][C]8.47138857306725[/C][C]-0.271388573067253[/C][/ROW]
[ROW][C]48[/C][C]8.7[/C][C]8.8761965869449[/C][C]-0.176196586944905[/C][/ROW]
[ROW][C]49[/C][C]8.3[/C][C]8.7988965641944[/C][C]-0.498896564194397[/C][/ROW]
[ROW][C]50[/C][C]7.9[/C][C]8.45308067294213[/C][C]-0.553080672942132[/C][/ROW]
[ROW][C]51[/C][C]7.5[/C][C]8.47545699531728[/C][C]-0.975456995317279[/C][/ROW]
[ROW][C]52[/C][C]7.8[/C][C]8.80703340869445[/C][C]-1.00703340869445[/C][/ROW]
[ROW][C]53[/C][C]8.3[/C][C]8.91077817607013[/C][C]-0.61077817607013[/C][/ROW]
[ROW][C]54[/C][C]8.4[/C][C]8.97790714319557[/C][C]-0.57790714319557[/C][/ROW]
[ROW][C]55[/C][C]8.2[/C][C]8.93315449844528[/C][C]-0.733154498445278[/C][/ROW]
[ROW][C]56[/C][C]7.7[/C][C]8.73176759706896[/C][C]-1.03176759706896[/C][/ROW]
[ROW][C]57[/C][C]7.2[/C][C]8.91484659832016[/C][C]-1.71484659832016[/C][/ROW]
[ROW][C]58[/C][C]7.3[/C][C]8.96163345419546[/C][C]-1.66163345419546[/C][/ROW]
[ROW][C]59[/C][C]8.1[/C][C]8.73990444156901[/C][C]-0.639904441569011[/C][/ROW]
[ROW][C]60[/C][C]8.5[/C][C]9.1487808776967[/C][C]-0.64878087769669[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57752&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57752&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
110.98.85992289794482.04007710205520
2108.508004373317491.49199562668251
39.28.5853043960680.614695603932001
49.28.715493908068850.484506091931148
59.58.762280763944160.737719236055842
69.68.90467554269510.695324457304909
79.58.878230798069920.621769201930083
89.18.699220219068740.400779780931255
98.98.7378702304440.162129769556002
1098.833478153319620.166521846680376
1110.18.512072795567521.58792720443248
1210.39.183362466821921.11663753317808
1310.28.882299220319941.31770077968006
149.68.526312273442611.07368772655739
159.28.674809685568580.525190314431415
169.38.61581756294320.684182437056802
179.48.748041286069060.651958713930936
189.48.81517025319450.584829746805496
199.28.780588664069280.419411335930721
2098.47952541756730.520474582432695
2198.780588664069280.219411335930722
2298.593441240568050.406558759431948
239.88.497833317692431.30216668230758
24109.091822966196320.908177033803683
259.88.78465708631931.01534291368070
269.38.49376489544240.806235104557602
2798.459183306317170.540816693682828
2898.532414906817650.467585093182348
299.18.75414391944410.345856080555895
309.18.672775474443570.427224525556428
319.18.687014952318660.412985047681335
329.28.412396450441870.787603549558134
338.88.670741263318560.129258736681442
348.38.60564650731813-0.305646507318131
358.48.44494382844208-0.0449438284420785
368.18.94739397632037-0.847393976320371
377.78.70939127469381-1.00939127469381
387.98.5405517513177-0.640551751317705
397.98.33916484994139-0.439164849941385
4088.5242780623176-0.524278062317599
417.98.79482814194437-0.894828141944371
427.68.57716755156794-0.977167551567946
437.18.53444911794267-1.43444911794267
446.88.59547545169307-1.79547545169307
456.58.44290961731707-1.94290961731707
466.98.60971492956816-1.70971492956816
478.28.47138857306725-0.271388573067253
488.78.8761965869449-0.176196586944905
498.38.7988965641944-0.498896564194397
507.98.45308067294213-0.553080672942132
517.58.47545699531728-0.975456995317279
527.88.80703340869445-1.00703340869445
538.38.91077817607013-0.61077817607013
548.48.97790714319557-0.57790714319557
558.28.93315449844528-0.733154498445278
567.78.73176759706896-1.03176759706896
577.28.91484659832016-1.71484659832016
587.38.96163345419546-1.66163345419546
598.18.73990444156901-0.639904441569011
608.59.1487808776967-0.64878087769669







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.431984301057270.863968602114540.56801569894273
60.3210574405162540.6421148810325090.678942559483746
70.2200581909109540.4401163818219070.779941809089046
80.1657115865296920.3314231730593840.834288413470308
90.1456788292560780.2913576585121570.854321170743922
100.1160319762182380.2320639524364760.883968023781762
110.1167758527713590.2335517055427180.883224147228641
120.1085432734517540.2170865469035070.891456726548246
130.1027066459551310.2054132919102630.897293354044869
140.07624728304986280.1524945660997260.923752716950137
150.05675984788560490.1135196957712100.943240152114395
160.03972322988446850.0794464597689370.960276770115531
170.02810084624088320.05620169248176640.971899153759117
180.02030830661900120.04061661323800230.979691693380999
190.01564404009017340.03128808018034690.984355959909827
200.01107718527476210.02215437054952430.988922814725238
210.009817137288590650.01963427457718130.99018286271141
220.007243891894304820.01448778378860960.992756108105695
230.01104424507038910.02208849014077820.988955754929611
240.01361546675532190.02723093351064380.986384533244678
250.01977733580234840.03955467160469680.980222664197652
260.02040901731989930.04081803463979870.9795909826801
270.01979251488480560.03958502976961130.980207485115194
280.02100955740733110.04201911481466210.97899044259267
290.02595300706125630.05190601412251260.974046992938744
300.03387105915915580.06774211831831170.966128940840844
310.04903760661521850.0980752132304370.950962393384782
320.1151411206934530.2302822413869070.884858879306547
330.1819350659193680.3638701318387370.818064934080632
340.2763068383153660.5526136766307320.723693161684634
350.3741571937469580.7483143874939150.625842806253042
360.5650991945872360.8698016108255280.434900805412764
370.7069534287827360.5860931424345290.293046571217264
380.742308084638290.515383830723420.25769191536171
390.7726540470778070.4546919058443850.227345952922193
400.7944968371983650.4110063256032710.205503162801635
410.8126214661044430.3747570677911140.187378533895557
420.8166137728339130.3667724543321740.183386227166087
430.850902296118080.2981954077638410.149097703881920
440.9237228079818640.1525543840362720.0762771920181362
450.9749165652994620.05016686940107530.0250834347005377
460.991822487079220.01635502584155940.00817751292077971
470.987654030807710.02469193838458040.0123459691922902
480.989923761353410.02015247729317920.0100762386465896
490.9851920601161710.02961587976765760.0148079398838288
500.9754954608053380.04900907838932440.0245045391946622
510.9519608691317660.0960782617364670.0480391308682335
520.9141620311634270.1716759376731450.0858379688365726
530.8682727504355340.2634544991289310.131727249564466
540.8124982098837440.3750035802325120.187501790116256
550.7097223473003870.5805553053992270.290277652699613

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.43198430105727 & 0.86396860211454 & 0.56801569894273 \tabularnewline
6 & 0.321057440516254 & 0.642114881032509 & 0.678942559483746 \tabularnewline
7 & 0.220058190910954 & 0.440116381821907 & 0.779941809089046 \tabularnewline
8 & 0.165711586529692 & 0.331423173059384 & 0.834288413470308 \tabularnewline
9 & 0.145678829256078 & 0.291357658512157 & 0.854321170743922 \tabularnewline
10 & 0.116031976218238 & 0.232063952436476 & 0.883968023781762 \tabularnewline
11 & 0.116775852771359 & 0.233551705542718 & 0.883224147228641 \tabularnewline
12 & 0.108543273451754 & 0.217086546903507 & 0.891456726548246 \tabularnewline
13 & 0.102706645955131 & 0.205413291910263 & 0.897293354044869 \tabularnewline
14 & 0.0762472830498628 & 0.152494566099726 & 0.923752716950137 \tabularnewline
15 & 0.0567598478856049 & 0.113519695771210 & 0.943240152114395 \tabularnewline
16 & 0.0397232298844685 & 0.079446459768937 & 0.960276770115531 \tabularnewline
17 & 0.0281008462408832 & 0.0562016924817664 & 0.971899153759117 \tabularnewline
18 & 0.0203083066190012 & 0.0406166132380023 & 0.979691693380999 \tabularnewline
19 & 0.0156440400901734 & 0.0312880801803469 & 0.984355959909827 \tabularnewline
20 & 0.0110771852747621 & 0.0221543705495243 & 0.988922814725238 \tabularnewline
21 & 0.00981713728859065 & 0.0196342745771813 & 0.99018286271141 \tabularnewline
22 & 0.00724389189430482 & 0.0144877837886096 & 0.992756108105695 \tabularnewline
23 & 0.0110442450703891 & 0.0220884901407782 & 0.988955754929611 \tabularnewline
24 & 0.0136154667553219 & 0.0272309335106438 & 0.986384533244678 \tabularnewline
25 & 0.0197773358023484 & 0.0395546716046968 & 0.980222664197652 \tabularnewline
26 & 0.0204090173198993 & 0.0408180346397987 & 0.9795909826801 \tabularnewline
27 & 0.0197925148848056 & 0.0395850297696113 & 0.980207485115194 \tabularnewline
28 & 0.0210095574073311 & 0.0420191148146621 & 0.97899044259267 \tabularnewline
29 & 0.0259530070612563 & 0.0519060141225126 & 0.974046992938744 \tabularnewline
30 & 0.0338710591591558 & 0.0677421183183117 & 0.966128940840844 \tabularnewline
31 & 0.0490376066152185 & 0.098075213230437 & 0.950962393384782 \tabularnewline
32 & 0.115141120693453 & 0.230282241386907 & 0.884858879306547 \tabularnewline
33 & 0.181935065919368 & 0.363870131838737 & 0.818064934080632 \tabularnewline
34 & 0.276306838315366 & 0.552613676630732 & 0.723693161684634 \tabularnewline
35 & 0.374157193746958 & 0.748314387493915 & 0.625842806253042 \tabularnewline
36 & 0.565099194587236 & 0.869801610825528 & 0.434900805412764 \tabularnewline
37 & 0.706953428782736 & 0.586093142434529 & 0.293046571217264 \tabularnewline
38 & 0.74230808463829 & 0.51538383072342 & 0.25769191536171 \tabularnewline
39 & 0.772654047077807 & 0.454691905844385 & 0.227345952922193 \tabularnewline
40 & 0.794496837198365 & 0.411006325603271 & 0.205503162801635 \tabularnewline
41 & 0.812621466104443 & 0.374757067791114 & 0.187378533895557 \tabularnewline
42 & 0.816613772833913 & 0.366772454332174 & 0.183386227166087 \tabularnewline
43 & 0.85090229611808 & 0.298195407763841 & 0.149097703881920 \tabularnewline
44 & 0.923722807981864 & 0.152554384036272 & 0.0762771920181362 \tabularnewline
45 & 0.974916565299462 & 0.0501668694010753 & 0.0250834347005377 \tabularnewline
46 & 0.99182248707922 & 0.0163550258415594 & 0.00817751292077971 \tabularnewline
47 & 0.98765403080771 & 0.0246919383845804 & 0.0123459691922902 \tabularnewline
48 & 0.98992376135341 & 0.0201524772931792 & 0.0100762386465896 \tabularnewline
49 & 0.985192060116171 & 0.0296158797676576 & 0.0148079398838288 \tabularnewline
50 & 0.975495460805338 & 0.0490090783893244 & 0.0245045391946622 \tabularnewline
51 & 0.951960869131766 & 0.096078261736467 & 0.0480391308682335 \tabularnewline
52 & 0.914162031163427 & 0.171675937673145 & 0.0858379688365726 \tabularnewline
53 & 0.868272750435534 & 0.263454499128931 & 0.131727249564466 \tabularnewline
54 & 0.812498209883744 & 0.375003580232512 & 0.187501790116256 \tabularnewline
55 & 0.709722347300387 & 0.580555305399227 & 0.290277652699613 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57752&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]0.43198430105727[/C][C]0.86396860211454[/C][C]0.56801569894273[/C][/ROW]
[ROW][C]6[/C][C]0.321057440516254[/C][C]0.642114881032509[/C][C]0.678942559483746[/C][/ROW]
[ROW][C]7[/C][C]0.220058190910954[/C][C]0.440116381821907[/C][C]0.779941809089046[/C][/ROW]
[ROW][C]8[/C][C]0.165711586529692[/C][C]0.331423173059384[/C][C]0.834288413470308[/C][/ROW]
[ROW][C]9[/C][C]0.145678829256078[/C][C]0.291357658512157[/C][C]0.854321170743922[/C][/ROW]
[ROW][C]10[/C][C]0.116031976218238[/C][C]0.232063952436476[/C][C]0.883968023781762[/C][/ROW]
[ROW][C]11[/C][C]0.116775852771359[/C][C]0.233551705542718[/C][C]0.883224147228641[/C][/ROW]
[ROW][C]12[/C][C]0.108543273451754[/C][C]0.217086546903507[/C][C]0.891456726548246[/C][/ROW]
[ROW][C]13[/C][C]0.102706645955131[/C][C]0.205413291910263[/C][C]0.897293354044869[/C][/ROW]
[ROW][C]14[/C][C]0.0762472830498628[/C][C]0.152494566099726[/C][C]0.923752716950137[/C][/ROW]
[ROW][C]15[/C][C]0.0567598478856049[/C][C]0.113519695771210[/C][C]0.943240152114395[/C][/ROW]
[ROW][C]16[/C][C]0.0397232298844685[/C][C]0.079446459768937[/C][C]0.960276770115531[/C][/ROW]
[ROW][C]17[/C][C]0.0281008462408832[/C][C]0.0562016924817664[/C][C]0.971899153759117[/C][/ROW]
[ROW][C]18[/C][C]0.0203083066190012[/C][C]0.0406166132380023[/C][C]0.979691693380999[/C][/ROW]
[ROW][C]19[/C][C]0.0156440400901734[/C][C]0.0312880801803469[/C][C]0.984355959909827[/C][/ROW]
[ROW][C]20[/C][C]0.0110771852747621[/C][C]0.0221543705495243[/C][C]0.988922814725238[/C][/ROW]
[ROW][C]21[/C][C]0.00981713728859065[/C][C]0.0196342745771813[/C][C]0.99018286271141[/C][/ROW]
[ROW][C]22[/C][C]0.00724389189430482[/C][C]0.0144877837886096[/C][C]0.992756108105695[/C][/ROW]
[ROW][C]23[/C][C]0.0110442450703891[/C][C]0.0220884901407782[/C][C]0.988955754929611[/C][/ROW]
[ROW][C]24[/C][C]0.0136154667553219[/C][C]0.0272309335106438[/C][C]0.986384533244678[/C][/ROW]
[ROW][C]25[/C][C]0.0197773358023484[/C][C]0.0395546716046968[/C][C]0.980222664197652[/C][/ROW]
[ROW][C]26[/C][C]0.0204090173198993[/C][C]0.0408180346397987[/C][C]0.9795909826801[/C][/ROW]
[ROW][C]27[/C][C]0.0197925148848056[/C][C]0.0395850297696113[/C][C]0.980207485115194[/C][/ROW]
[ROW][C]28[/C][C]0.0210095574073311[/C][C]0.0420191148146621[/C][C]0.97899044259267[/C][/ROW]
[ROW][C]29[/C][C]0.0259530070612563[/C][C]0.0519060141225126[/C][C]0.974046992938744[/C][/ROW]
[ROW][C]30[/C][C]0.0338710591591558[/C][C]0.0677421183183117[/C][C]0.966128940840844[/C][/ROW]
[ROW][C]31[/C][C]0.0490376066152185[/C][C]0.098075213230437[/C][C]0.950962393384782[/C][/ROW]
[ROW][C]32[/C][C]0.115141120693453[/C][C]0.230282241386907[/C][C]0.884858879306547[/C][/ROW]
[ROW][C]33[/C][C]0.181935065919368[/C][C]0.363870131838737[/C][C]0.818064934080632[/C][/ROW]
[ROW][C]34[/C][C]0.276306838315366[/C][C]0.552613676630732[/C][C]0.723693161684634[/C][/ROW]
[ROW][C]35[/C][C]0.374157193746958[/C][C]0.748314387493915[/C][C]0.625842806253042[/C][/ROW]
[ROW][C]36[/C][C]0.565099194587236[/C][C]0.869801610825528[/C][C]0.434900805412764[/C][/ROW]
[ROW][C]37[/C][C]0.706953428782736[/C][C]0.586093142434529[/C][C]0.293046571217264[/C][/ROW]
[ROW][C]38[/C][C]0.74230808463829[/C][C]0.51538383072342[/C][C]0.25769191536171[/C][/ROW]
[ROW][C]39[/C][C]0.772654047077807[/C][C]0.454691905844385[/C][C]0.227345952922193[/C][/ROW]
[ROW][C]40[/C][C]0.794496837198365[/C][C]0.411006325603271[/C][C]0.205503162801635[/C][/ROW]
[ROW][C]41[/C][C]0.812621466104443[/C][C]0.374757067791114[/C][C]0.187378533895557[/C][/ROW]
[ROW][C]42[/C][C]0.816613772833913[/C][C]0.366772454332174[/C][C]0.183386227166087[/C][/ROW]
[ROW][C]43[/C][C]0.85090229611808[/C][C]0.298195407763841[/C][C]0.149097703881920[/C][/ROW]
[ROW][C]44[/C][C]0.923722807981864[/C][C]0.152554384036272[/C][C]0.0762771920181362[/C][/ROW]
[ROW][C]45[/C][C]0.974916565299462[/C][C]0.0501668694010753[/C][C]0.0250834347005377[/C][/ROW]
[ROW][C]46[/C][C]0.99182248707922[/C][C]0.0163550258415594[/C][C]0.00817751292077971[/C][/ROW]
[ROW][C]47[/C][C]0.98765403080771[/C][C]0.0246919383845804[/C][C]0.0123459691922902[/C][/ROW]
[ROW][C]48[/C][C]0.98992376135341[/C][C]0.0201524772931792[/C][C]0.0100762386465896[/C][/ROW]
[ROW][C]49[/C][C]0.985192060116171[/C][C]0.0296158797676576[/C][C]0.0148079398838288[/C][/ROW]
[ROW][C]50[/C][C]0.975495460805338[/C][C]0.0490090783893244[/C][C]0.0245045391946622[/C][/ROW]
[ROW][C]51[/C][C]0.951960869131766[/C][C]0.096078261736467[/C][C]0.0480391308682335[/C][/ROW]
[ROW][C]52[/C][C]0.914162031163427[/C][C]0.171675937673145[/C][C]0.0858379688365726[/C][/ROW]
[ROW][C]53[/C][C]0.868272750435534[/C][C]0.263454499128931[/C][C]0.131727249564466[/C][/ROW]
[ROW][C]54[/C][C]0.812498209883744[/C][C]0.375003580232512[/C][C]0.187501790116256[/C][/ROW]
[ROW][C]55[/C][C]0.709722347300387[/C][C]0.580555305399227[/C][C]0.290277652699613[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57752&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57752&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
50.431984301057270.863968602114540.56801569894273
60.3210574405162540.6421148810325090.678942559483746
70.2200581909109540.4401163818219070.779941809089046
80.1657115865296920.3314231730593840.834288413470308
90.1456788292560780.2913576585121570.854321170743922
100.1160319762182380.2320639524364760.883968023781762
110.1167758527713590.2335517055427180.883224147228641
120.1085432734517540.2170865469035070.891456726548246
130.1027066459551310.2054132919102630.897293354044869
140.07624728304986280.1524945660997260.923752716950137
150.05675984788560490.1135196957712100.943240152114395
160.03972322988446850.0794464597689370.960276770115531
170.02810084624088320.05620169248176640.971899153759117
180.02030830661900120.04061661323800230.979691693380999
190.01564404009017340.03128808018034690.984355959909827
200.01107718527476210.02215437054952430.988922814725238
210.009817137288590650.01963427457718130.99018286271141
220.007243891894304820.01448778378860960.992756108105695
230.01104424507038910.02208849014077820.988955754929611
240.01361546675532190.02723093351064380.986384533244678
250.01977733580234840.03955467160469680.980222664197652
260.02040901731989930.04081803463979870.9795909826801
270.01979251488480560.03958502976961130.980207485115194
280.02100955740733110.04201911481466210.97899044259267
290.02595300706125630.05190601412251260.974046992938744
300.03387105915915580.06774211831831170.966128940840844
310.04903760661521850.0980752132304370.950962393384782
320.1151411206934530.2302822413869070.884858879306547
330.1819350659193680.3638701318387370.818064934080632
340.2763068383153660.5526136766307320.723693161684634
350.3741571937469580.7483143874939150.625842806253042
360.5650991945872360.8698016108255280.434900805412764
370.7069534287827360.5860931424345290.293046571217264
380.742308084638290.515383830723420.25769191536171
390.7726540470778070.4546919058443850.227345952922193
400.7944968371983650.4110063256032710.205503162801635
410.8126214661044430.3747570677911140.187378533895557
420.8166137728339130.3667724543321740.183386227166087
430.850902296118080.2981954077638410.149097703881920
440.9237228079818640.1525543840362720.0762771920181362
450.9749165652994620.05016686940107530.0250834347005377
460.991822487079220.01635502584155940.00817751292077971
470.987654030807710.02469193838458040.0123459691922902
480.989923761353410.02015247729317920.0100762386465896
490.9851920601161710.02961587976765760.0148079398838288
500.9754954608053380.04900907838932440.0245045391946622
510.9519608691317660.0960782617364670.0480391308682335
520.9141620311634270.1716759376731450.0858379688365726
530.8682727504355340.2634544991289310.131727249564466
540.8124982098837440.3750035802325120.187501790116256
550.7097223473003870.5805553053992270.290277652699613







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level160.313725490196078NOK
10% type I error level230.450980392156863NOK

\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 & 16 & 0.313725490196078 & NOK \tabularnewline
10% type I error level & 23 & 0.450980392156863 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57752&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]16[/C][C]0.313725490196078[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]23[/C][C]0.450980392156863[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57752&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57752&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 level00OK
5% type I error level160.313725490196078NOK
10% type I error level230.450980392156863NOK



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')
}