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muliple regression_basic. indexcijfer van de totale industriële productie &...

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 08:13:51 -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/t1258643952mpqnlalfe9cp583.htm/, Retrieved Fri, 29 Mar 2024 02:05:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57756, Retrieved Fri, 29 Mar 2024 02:05:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact136
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] [muliple regressio...] [2009-11-19 15:13:51] [8f072ead2c7c0b3cf3fdae49bab9dd9b] [Current]
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Dataseries X:
95.1	117.1
97	118.7
112.7	126.5
102.9	127.5
97.4	134.6
111.4	131.8
87.4	135.9
96.8	142.7
114.1	141.7
110.3	153.4
103.9	145
101.6	137.7
94.6	148.3
95.9	152.2
104.7	169.4
102.8	168.6
98.1	161.1
113.9	174.1
80.9	179
95.7	190.6
113.2	190
105.9	181.6
108.8	174.8
102.3	180.5
99	196.8
100.7	193.8
115.5	197
100.7	216.3
109.9	221.4
114.6	217.9
85.4	229.7
100.5	227.4
114.8	204.2
116.5	196.6
112.9	198.8
102	207.5
106	190.7
105.3	201.6
118.8	210.5
106.1	223.5
109.3	223.8
117.2	231.2
92.5	244
104.2	234.7
112.5	250.2
122.4	265.7
113.3	287.6
100	283.3
110.7	295.4
112.8	312.3
109.8	333.8
117.3	347.7
109.1	383.2
115.9	407.1
96	413.6
99.8	362.7
116.8	321.9
115.7	239.4
99.4	191
94.3	159.7
91	163.4




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=57756&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=57756&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57756&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
tot.ind.prod.index[t] = + 97.0651490600515 + 0.0381800024164384prijsindex.grondst.incl.energie[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
tot.ind.prod.index[t] =  +  97.0651490600515 +  0.0381800024164384prijsindex.grondst.incl.energie[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57756&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]tot.ind.prod.index[t] =  +  97.0651490600515 +  0.0381800024164384prijsindex.grondst.incl.energie[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57756&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57756&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
tot.ind.prod.index[t] = + 97.0651490600515 + 0.0381800024164384prijsindex.grondst.incl.energie[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)97.06514906005153.50987727.654900
prijsindex.grondst.incl.energie0.03818000241643840.0156442.44050.0176890.008845

\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) & 97.0651490600515 & 3.509877 & 27.6549 & 0 & 0 \tabularnewline
prijsindex.grondst.incl.energie & 0.0381800024164384 & 0.015644 & 2.4405 & 0.017689 & 0.008845 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57756&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]97.0651490600515[/C][C]3.509877[/C][C]27.6549[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]prijsindex.grondst.incl.energie[/C][C]0.0381800024164384[/C][C]0.015644[/C][C]2.4405[/C][C]0.017689[/C][C]0.008845[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57756&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57756&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)97.06514906005153.50987727.654900
prijsindex.grondst.incl.energie0.03818000241643840.0156442.44050.0176890.008845







Multiple Linear Regression - Regression Statistics
Multiple R0.302807907985026
R-squared0.091692629138268
Adjusted R-squared0.0762975889541708
F-TEST (value)5.95598504724821
F-TEST (DF numerator)1
F-TEST (DF denominator)59
p-value0.0176891166963650
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8.75875544910125
Sum Squared Residuals4526.23202401249

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.302807907985026 \tabularnewline
R-squared & 0.091692629138268 \tabularnewline
Adjusted R-squared & 0.0762975889541708 \tabularnewline
F-TEST (value) & 5.95598504724821 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 59 \tabularnewline
p-value & 0.0176891166963650 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 8.75875544910125 \tabularnewline
Sum Squared Residuals & 4526.23202401249 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57756&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.302807907985026[/C][/ROW]
[ROW][C]R-squared[/C][C]0.091692629138268[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0762975889541708[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5.95598504724821[/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]0.0176891166963650[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]8.75875544910125[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]4526.23202401249[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57756&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57756&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 R0.302807907985026
R-squared0.091692629138268
Adjusted R-squared0.0762975889541708
F-TEST (value)5.95598504724821
F-TEST (DF numerator)1
F-TEST (DF denominator)59
p-value0.0176891166963650
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8.75875544910125
Sum Squared Residuals4526.23202401249







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
195.1101.536027343016-6.43602734301649
297101.597115346883-4.59711534688277
3112.7101.89491936573110.805080634269
4102.9101.9330993681470.966900631852572
597.4102.204177385304-4.80417738530414
6111.4102.0972733785389.30272662146189
787.4102.253811388446-14.8538113884455
896.8102.513435404877-5.7134354048773
9114.1102.47525540246111.6247445975391
10110.3102.9219614307337.37803856926681
11103.9102.6012494104351.2987505895649
12101.6102.322535392795-0.72253539279511
1394.6102.727243418409-8.12724341840936
1495.9102.876145427833-6.97614542783346
15104.7103.5328414693961.16715853060380
16102.8103.502297467463-0.702297467463053
1798.1103.215947449340-5.11594744933977
18113.9103.71228748075310.1877125192465
1980.9103.899369492594-22.999369492594
2095.7104.342257520625-8.6422575206247
21113.2104.3193495191758.88065048082517
22105.9103.9986374988771.90136250112326
23108.8103.7390134824455.06098651755503
24102.3103.956639496219-1.65663949621867
2599104.578973535607-5.57897353560661
26100.7104.464433528357-3.76443352835729
27115.5104.5866095360910.9133904639101
28100.7105.323483582727-4.62348358272716
29109.9105.5182015950514.38179840494901
30114.6105.3845715865939.21542841340653
3185.4105.835095615107-20.4350956151074
32100.5105.747281609550-5.24728160954963
33114.8104.8615055534889.93849444651174
34116.5104.57133753512311.9286624648767
35112.9104.6553335404398.24466645956052
36102104.987499561462-2.9874995614625
37106104.3460755208661.65392447913366
38105.3104.7622375472060.537762452794481
39118.8105.10203956871213.6979604312882
40106.1105.5983796001260.501620399874478
41109.3105.6098336008503.69016639914955
42117.2105.89236561873211.3076343812679
4392.5106.381069649663-13.8810696496625
44104.2106.025995627190-1.82599562718962
45112.5106.6177856646445.88221433535558
46122.4107.20957570209915.1904242979008
47113.3108.0457177550195.25428224498078
48100107.881543744629-7.88154374462853
49110.7108.3435217738672.35647822613257
50112.8108.9887638147053.81123618529475
51109.8109.809633866659-0.00963386665867005
52117.3110.3403359002476.95966409975284
53109.1111.695725986031-2.59572598603073
54115.9112.6082280437843.29177195621641
5596112.856398059490-16.8563980594904
5699.8110.913035936494-11.1130359364937
57116.8109.3552918379037.44470816209695
58115.7106.2054416385479.49455836145312
5999.4104.357529521591-4.95752952159126
6094.3103.162495445957-8.86249544595675
6191103.303761454898-12.3037614548976

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 95.1 & 101.536027343016 & -6.43602734301649 \tabularnewline
2 & 97 & 101.597115346883 & -4.59711534688277 \tabularnewline
3 & 112.7 & 101.894919365731 & 10.805080634269 \tabularnewline
4 & 102.9 & 101.933099368147 & 0.966900631852572 \tabularnewline
5 & 97.4 & 102.204177385304 & -4.80417738530414 \tabularnewline
6 & 111.4 & 102.097273378538 & 9.30272662146189 \tabularnewline
7 & 87.4 & 102.253811388446 & -14.8538113884455 \tabularnewline
8 & 96.8 & 102.513435404877 & -5.7134354048773 \tabularnewline
9 & 114.1 & 102.475255402461 & 11.6247445975391 \tabularnewline
10 & 110.3 & 102.921961430733 & 7.37803856926681 \tabularnewline
11 & 103.9 & 102.601249410435 & 1.2987505895649 \tabularnewline
12 & 101.6 & 102.322535392795 & -0.72253539279511 \tabularnewline
13 & 94.6 & 102.727243418409 & -8.12724341840936 \tabularnewline
14 & 95.9 & 102.876145427833 & -6.97614542783346 \tabularnewline
15 & 104.7 & 103.532841469396 & 1.16715853060380 \tabularnewline
16 & 102.8 & 103.502297467463 & -0.702297467463053 \tabularnewline
17 & 98.1 & 103.215947449340 & -5.11594744933977 \tabularnewline
18 & 113.9 & 103.712287480753 & 10.1877125192465 \tabularnewline
19 & 80.9 & 103.899369492594 & -22.999369492594 \tabularnewline
20 & 95.7 & 104.342257520625 & -8.6422575206247 \tabularnewline
21 & 113.2 & 104.319349519175 & 8.88065048082517 \tabularnewline
22 & 105.9 & 103.998637498877 & 1.90136250112326 \tabularnewline
23 & 108.8 & 103.739013482445 & 5.06098651755503 \tabularnewline
24 & 102.3 & 103.956639496219 & -1.65663949621867 \tabularnewline
25 & 99 & 104.578973535607 & -5.57897353560661 \tabularnewline
26 & 100.7 & 104.464433528357 & -3.76443352835729 \tabularnewline
27 & 115.5 & 104.58660953609 & 10.9133904639101 \tabularnewline
28 & 100.7 & 105.323483582727 & -4.62348358272716 \tabularnewline
29 & 109.9 & 105.518201595051 & 4.38179840494901 \tabularnewline
30 & 114.6 & 105.384571586593 & 9.21542841340653 \tabularnewline
31 & 85.4 & 105.835095615107 & -20.4350956151074 \tabularnewline
32 & 100.5 & 105.747281609550 & -5.24728160954963 \tabularnewline
33 & 114.8 & 104.861505553488 & 9.93849444651174 \tabularnewline
34 & 116.5 & 104.571337535123 & 11.9286624648767 \tabularnewline
35 & 112.9 & 104.655333540439 & 8.24466645956052 \tabularnewline
36 & 102 & 104.987499561462 & -2.9874995614625 \tabularnewline
37 & 106 & 104.346075520866 & 1.65392447913366 \tabularnewline
38 & 105.3 & 104.762237547206 & 0.537762452794481 \tabularnewline
39 & 118.8 & 105.102039568712 & 13.6979604312882 \tabularnewline
40 & 106.1 & 105.598379600126 & 0.501620399874478 \tabularnewline
41 & 109.3 & 105.609833600850 & 3.69016639914955 \tabularnewline
42 & 117.2 & 105.892365618732 & 11.3076343812679 \tabularnewline
43 & 92.5 & 106.381069649663 & -13.8810696496625 \tabularnewline
44 & 104.2 & 106.025995627190 & -1.82599562718962 \tabularnewline
45 & 112.5 & 106.617785664644 & 5.88221433535558 \tabularnewline
46 & 122.4 & 107.209575702099 & 15.1904242979008 \tabularnewline
47 & 113.3 & 108.045717755019 & 5.25428224498078 \tabularnewline
48 & 100 & 107.881543744629 & -7.88154374462853 \tabularnewline
49 & 110.7 & 108.343521773867 & 2.35647822613257 \tabularnewline
50 & 112.8 & 108.988763814705 & 3.81123618529475 \tabularnewline
51 & 109.8 & 109.809633866659 & -0.00963386665867005 \tabularnewline
52 & 117.3 & 110.340335900247 & 6.95966409975284 \tabularnewline
53 & 109.1 & 111.695725986031 & -2.59572598603073 \tabularnewline
54 & 115.9 & 112.608228043784 & 3.29177195621641 \tabularnewline
55 & 96 & 112.856398059490 & -16.8563980594904 \tabularnewline
56 & 99.8 & 110.913035936494 & -11.1130359364937 \tabularnewline
57 & 116.8 & 109.355291837903 & 7.44470816209695 \tabularnewline
58 & 115.7 & 106.205441638547 & 9.49455836145312 \tabularnewline
59 & 99.4 & 104.357529521591 & -4.95752952159126 \tabularnewline
60 & 94.3 & 103.162495445957 & -8.86249544595675 \tabularnewline
61 & 91 & 103.303761454898 & -12.3037614548976 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57756&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]95.1[/C][C]101.536027343016[/C][C]-6.43602734301649[/C][/ROW]
[ROW][C]2[/C][C]97[/C][C]101.597115346883[/C][C]-4.59711534688277[/C][/ROW]
[ROW][C]3[/C][C]112.7[/C][C]101.894919365731[/C][C]10.805080634269[/C][/ROW]
[ROW][C]4[/C][C]102.9[/C][C]101.933099368147[/C][C]0.966900631852572[/C][/ROW]
[ROW][C]5[/C][C]97.4[/C][C]102.204177385304[/C][C]-4.80417738530414[/C][/ROW]
[ROW][C]6[/C][C]111.4[/C][C]102.097273378538[/C][C]9.30272662146189[/C][/ROW]
[ROW][C]7[/C][C]87.4[/C][C]102.253811388446[/C][C]-14.8538113884455[/C][/ROW]
[ROW][C]8[/C][C]96.8[/C][C]102.513435404877[/C][C]-5.7134354048773[/C][/ROW]
[ROW][C]9[/C][C]114.1[/C][C]102.475255402461[/C][C]11.6247445975391[/C][/ROW]
[ROW][C]10[/C][C]110.3[/C][C]102.921961430733[/C][C]7.37803856926681[/C][/ROW]
[ROW][C]11[/C][C]103.9[/C][C]102.601249410435[/C][C]1.2987505895649[/C][/ROW]
[ROW][C]12[/C][C]101.6[/C][C]102.322535392795[/C][C]-0.72253539279511[/C][/ROW]
[ROW][C]13[/C][C]94.6[/C][C]102.727243418409[/C][C]-8.12724341840936[/C][/ROW]
[ROW][C]14[/C][C]95.9[/C][C]102.876145427833[/C][C]-6.97614542783346[/C][/ROW]
[ROW][C]15[/C][C]104.7[/C][C]103.532841469396[/C][C]1.16715853060380[/C][/ROW]
[ROW][C]16[/C][C]102.8[/C][C]103.502297467463[/C][C]-0.702297467463053[/C][/ROW]
[ROW][C]17[/C][C]98.1[/C][C]103.215947449340[/C][C]-5.11594744933977[/C][/ROW]
[ROW][C]18[/C][C]113.9[/C][C]103.712287480753[/C][C]10.1877125192465[/C][/ROW]
[ROW][C]19[/C][C]80.9[/C][C]103.899369492594[/C][C]-22.999369492594[/C][/ROW]
[ROW][C]20[/C][C]95.7[/C][C]104.342257520625[/C][C]-8.6422575206247[/C][/ROW]
[ROW][C]21[/C][C]113.2[/C][C]104.319349519175[/C][C]8.88065048082517[/C][/ROW]
[ROW][C]22[/C][C]105.9[/C][C]103.998637498877[/C][C]1.90136250112326[/C][/ROW]
[ROW][C]23[/C][C]108.8[/C][C]103.739013482445[/C][C]5.06098651755503[/C][/ROW]
[ROW][C]24[/C][C]102.3[/C][C]103.956639496219[/C][C]-1.65663949621867[/C][/ROW]
[ROW][C]25[/C][C]99[/C][C]104.578973535607[/C][C]-5.57897353560661[/C][/ROW]
[ROW][C]26[/C][C]100.7[/C][C]104.464433528357[/C][C]-3.76443352835729[/C][/ROW]
[ROW][C]27[/C][C]115.5[/C][C]104.58660953609[/C][C]10.9133904639101[/C][/ROW]
[ROW][C]28[/C][C]100.7[/C][C]105.323483582727[/C][C]-4.62348358272716[/C][/ROW]
[ROW][C]29[/C][C]109.9[/C][C]105.518201595051[/C][C]4.38179840494901[/C][/ROW]
[ROW][C]30[/C][C]114.6[/C][C]105.384571586593[/C][C]9.21542841340653[/C][/ROW]
[ROW][C]31[/C][C]85.4[/C][C]105.835095615107[/C][C]-20.4350956151074[/C][/ROW]
[ROW][C]32[/C][C]100.5[/C][C]105.747281609550[/C][C]-5.24728160954963[/C][/ROW]
[ROW][C]33[/C][C]114.8[/C][C]104.861505553488[/C][C]9.93849444651174[/C][/ROW]
[ROW][C]34[/C][C]116.5[/C][C]104.571337535123[/C][C]11.9286624648767[/C][/ROW]
[ROW][C]35[/C][C]112.9[/C][C]104.655333540439[/C][C]8.24466645956052[/C][/ROW]
[ROW][C]36[/C][C]102[/C][C]104.987499561462[/C][C]-2.9874995614625[/C][/ROW]
[ROW][C]37[/C][C]106[/C][C]104.346075520866[/C][C]1.65392447913366[/C][/ROW]
[ROW][C]38[/C][C]105.3[/C][C]104.762237547206[/C][C]0.537762452794481[/C][/ROW]
[ROW][C]39[/C][C]118.8[/C][C]105.102039568712[/C][C]13.6979604312882[/C][/ROW]
[ROW][C]40[/C][C]106.1[/C][C]105.598379600126[/C][C]0.501620399874478[/C][/ROW]
[ROW][C]41[/C][C]109.3[/C][C]105.609833600850[/C][C]3.69016639914955[/C][/ROW]
[ROW][C]42[/C][C]117.2[/C][C]105.892365618732[/C][C]11.3076343812679[/C][/ROW]
[ROW][C]43[/C][C]92.5[/C][C]106.381069649663[/C][C]-13.8810696496625[/C][/ROW]
[ROW][C]44[/C][C]104.2[/C][C]106.025995627190[/C][C]-1.82599562718962[/C][/ROW]
[ROW][C]45[/C][C]112.5[/C][C]106.617785664644[/C][C]5.88221433535558[/C][/ROW]
[ROW][C]46[/C][C]122.4[/C][C]107.209575702099[/C][C]15.1904242979008[/C][/ROW]
[ROW][C]47[/C][C]113.3[/C][C]108.045717755019[/C][C]5.25428224498078[/C][/ROW]
[ROW][C]48[/C][C]100[/C][C]107.881543744629[/C][C]-7.88154374462853[/C][/ROW]
[ROW][C]49[/C][C]110.7[/C][C]108.343521773867[/C][C]2.35647822613257[/C][/ROW]
[ROW][C]50[/C][C]112.8[/C][C]108.988763814705[/C][C]3.81123618529475[/C][/ROW]
[ROW][C]51[/C][C]109.8[/C][C]109.809633866659[/C][C]-0.00963386665867005[/C][/ROW]
[ROW][C]52[/C][C]117.3[/C][C]110.340335900247[/C][C]6.95966409975284[/C][/ROW]
[ROW][C]53[/C][C]109.1[/C][C]111.695725986031[/C][C]-2.59572598603073[/C][/ROW]
[ROW][C]54[/C][C]115.9[/C][C]112.608228043784[/C][C]3.29177195621641[/C][/ROW]
[ROW][C]55[/C][C]96[/C][C]112.856398059490[/C][C]-16.8563980594904[/C][/ROW]
[ROW][C]56[/C][C]99.8[/C][C]110.913035936494[/C][C]-11.1130359364937[/C][/ROW]
[ROW][C]57[/C][C]116.8[/C][C]109.355291837903[/C][C]7.44470816209695[/C][/ROW]
[ROW][C]58[/C][C]115.7[/C][C]106.205441638547[/C][C]9.49455836145312[/C][/ROW]
[ROW][C]59[/C][C]99.4[/C][C]104.357529521591[/C][C]-4.95752952159126[/C][/ROW]
[ROW][C]60[/C][C]94.3[/C][C]103.162495445957[/C][C]-8.86249544595675[/C][/ROW]
[ROW][C]61[/C][C]91[/C][C]103.303761454898[/C][C]-12.3037614548976[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57756&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57756&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
195.1101.536027343016-6.43602734301649
297101.597115346883-4.59711534688277
3112.7101.89491936573110.805080634269
4102.9101.9330993681470.966900631852572
597.4102.204177385304-4.80417738530414
6111.4102.0972733785389.30272662146189
787.4102.253811388446-14.8538113884455
896.8102.513435404877-5.7134354048773
9114.1102.47525540246111.6247445975391
10110.3102.9219614307337.37803856926681
11103.9102.6012494104351.2987505895649
12101.6102.322535392795-0.72253539279511
1394.6102.727243418409-8.12724341840936
1495.9102.876145427833-6.97614542783346
15104.7103.5328414693961.16715853060380
16102.8103.502297467463-0.702297467463053
1798.1103.215947449340-5.11594744933977
18113.9103.71228748075310.1877125192465
1980.9103.899369492594-22.999369492594
2095.7104.342257520625-8.6422575206247
21113.2104.3193495191758.88065048082517
22105.9103.9986374988771.90136250112326
23108.8103.7390134824455.06098651755503
24102.3103.956639496219-1.65663949621867
2599104.578973535607-5.57897353560661
26100.7104.464433528357-3.76443352835729
27115.5104.5866095360910.9133904639101
28100.7105.323483582727-4.62348358272716
29109.9105.5182015950514.38179840494901
30114.6105.3845715865939.21542841340653
3185.4105.835095615107-20.4350956151074
32100.5105.747281609550-5.24728160954963
33114.8104.8615055534889.93849444651174
34116.5104.57133753512311.9286624648767
35112.9104.6553335404398.24466645956052
36102104.987499561462-2.9874995614625
37106104.3460755208661.65392447913366
38105.3104.7622375472060.537762452794481
39118.8105.10203956871213.6979604312882
40106.1105.5983796001260.501620399874478
41109.3105.6098336008503.69016639914955
42117.2105.89236561873211.3076343812679
4392.5106.381069649663-13.8810696496625
44104.2106.025995627190-1.82599562718962
45112.5106.6177856646445.88221433535558
46122.4107.20957570209915.1904242979008
47113.3108.0457177550195.25428224498078
48100107.881543744629-7.88154374462853
49110.7108.3435217738672.35647822613257
50112.8108.9887638147053.81123618529475
51109.8109.809633866659-0.00963386665867005
52117.3110.3403359002476.95966409975284
53109.1111.695725986031-2.59572598603073
54115.9112.6082280437843.29177195621641
5596112.856398059490-16.8563980594904
5699.8110.913035936494-11.1130359364937
57116.8109.3552918379037.44470816209695
58115.7106.2054416385479.49455836145312
5999.4104.357529521591-4.95752952159126
6094.3103.162495445957-8.86249544595675
6191103.303761454898-12.3037614548976







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.488520380557930.977040761115860.51147961944207
60.4284589102747130.8569178205494260.571541089725287
70.715570302721880.568859394556240.28442969727812
80.6092867367053440.7814265265893110.390713263294656
90.702663262950860.594673474098280.29733673704914
100.6252078677142160.7495842645715680.374792132285784
110.5216073121232540.9567853757534930.478392687876746
120.4211907750494210.8423815500988420.578809224950579
130.4401102859327320.8802205718654640.559889714067268
140.4084542495764880.8169084991529760.591545750423512
150.3212801513934560.6425603027869110.678719848606544
160.2441190436103010.4882380872206020.755880956389699
170.1983371637133230.3966743274266460.801662836286677
180.2162735558221110.4325471116442210.78372644417789
190.6464238462692380.7071523074615240.353576153730762
200.6094201937524770.7811596124950470.390579806247523
210.654241850888290.691516298223420.34575814911171
220.5876541587420850.824691682515830.412345841257915
230.5390670372262340.9218659255475320.460932962773766
240.4645228379432020.9290456758864040.535477162056798
250.4153264267310820.8306528534621640.584673573268918
260.3543911533215910.7087823066431820.64560884667841
270.3970083162968130.7940166325936260.602991683703187
280.3454353672548430.6908707345096860.654564632745157
290.2938500435653770.5877000871307550.706149956434623
300.2881895128609580.5763790257219170.711810487139042
310.6121332718868260.7757334562263480.387866728113174
320.5670736238230130.8658527523539730.432926376176987
330.580764151919580.838471696160840.41923584808042
340.6267399263172290.7465201473655420.373260073682771
350.6085361229725550.782927754054890.391463877027445
360.5442587074184280.9114825851631440.455741292581572
370.4681841085570910.9363682171141810.531815891442909
380.3916736037269950.7833472074539910.608326396273005
390.4829372440437090.9658744880874170.517062755956291
400.4043060755726350.808612151145270.595693924427365
410.3403213620294210.6806427240588420.659678637970579
420.3877173569448430.7754347138896850.612282643055157
430.495367857030220.990735714060440.50463214296978
440.4136000099610270.8272000199220530.586399990038973
450.3678507111029470.7357014222058940.632149288897053
460.5547321454376480.8905357091247040.445267854562352
470.5142101148875980.9715797702248040.485789885112402
480.4777618514498730.9555237028997460.522238148550127
490.3991286947658670.7982573895317330.600871305234133
500.33822286111410.67644572222820.6617771388859
510.2540367445744180.5080734891488350.745963255425582
520.2528872597621760.5057745195243520.747112740237824
530.1749355621413970.3498711242827950.825064437858603
540.1503187553984360.3006375107968730.849681244601564
550.2490778531810750.4981557063621510.750922146818925
560.7223288157317940.5553423685364110.277671184268206

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.48852038055793 & 0.97704076111586 & 0.51147961944207 \tabularnewline
6 & 0.428458910274713 & 0.856917820549426 & 0.571541089725287 \tabularnewline
7 & 0.71557030272188 & 0.56885939455624 & 0.28442969727812 \tabularnewline
8 & 0.609286736705344 & 0.781426526589311 & 0.390713263294656 \tabularnewline
9 & 0.70266326295086 & 0.59467347409828 & 0.29733673704914 \tabularnewline
10 & 0.625207867714216 & 0.749584264571568 & 0.374792132285784 \tabularnewline
11 & 0.521607312123254 & 0.956785375753493 & 0.478392687876746 \tabularnewline
12 & 0.421190775049421 & 0.842381550098842 & 0.578809224950579 \tabularnewline
13 & 0.440110285932732 & 0.880220571865464 & 0.559889714067268 \tabularnewline
14 & 0.408454249576488 & 0.816908499152976 & 0.591545750423512 \tabularnewline
15 & 0.321280151393456 & 0.642560302786911 & 0.678719848606544 \tabularnewline
16 & 0.244119043610301 & 0.488238087220602 & 0.755880956389699 \tabularnewline
17 & 0.198337163713323 & 0.396674327426646 & 0.801662836286677 \tabularnewline
18 & 0.216273555822111 & 0.432547111644221 & 0.78372644417789 \tabularnewline
19 & 0.646423846269238 & 0.707152307461524 & 0.353576153730762 \tabularnewline
20 & 0.609420193752477 & 0.781159612495047 & 0.390579806247523 \tabularnewline
21 & 0.65424185088829 & 0.69151629822342 & 0.34575814911171 \tabularnewline
22 & 0.587654158742085 & 0.82469168251583 & 0.412345841257915 \tabularnewline
23 & 0.539067037226234 & 0.921865925547532 & 0.460932962773766 \tabularnewline
24 & 0.464522837943202 & 0.929045675886404 & 0.535477162056798 \tabularnewline
25 & 0.415326426731082 & 0.830652853462164 & 0.584673573268918 \tabularnewline
26 & 0.354391153321591 & 0.708782306643182 & 0.64560884667841 \tabularnewline
27 & 0.397008316296813 & 0.794016632593626 & 0.602991683703187 \tabularnewline
28 & 0.345435367254843 & 0.690870734509686 & 0.654564632745157 \tabularnewline
29 & 0.293850043565377 & 0.587700087130755 & 0.706149956434623 \tabularnewline
30 & 0.288189512860958 & 0.576379025721917 & 0.711810487139042 \tabularnewline
31 & 0.612133271886826 & 0.775733456226348 & 0.387866728113174 \tabularnewline
32 & 0.567073623823013 & 0.865852752353973 & 0.432926376176987 \tabularnewline
33 & 0.58076415191958 & 0.83847169616084 & 0.41923584808042 \tabularnewline
34 & 0.626739926317229 & 0.746520147365542 & 0.373260073682771 \tabularnewline
35 & 0.608536122972555 & 0.78292775405489 & 0.391463877027445 \tabularnewline
36 & 0.544258707418428 & 0.911482585163144 & 0.455741292581572 \tabularnewline
37 & 0.468184108557091 & 0.936368217114181 & 0.531815891442909 \tabularnewline
38 & 0.391673603726995 & 0.783347207453991 & 0.608326396273005 \tabularnewline
39 & 0.482937244043709 & 0.965874488087417 & 0.517062755956291 \tabularnewline
40 & 0.404306075572635 & 0.80861215114527 & 0.595693924427365 \tabularnewline
41 & 0.340321362029421 & 0.680642724058842 & 0.659678637970579 \tabularnewline
42 & 0.387717356944843 & 0.775434713889685 & 0.612282643055157 \tabularnewline
43 & 0.49536785703022 & 0.99073571406044 & 0.50463214296978 \tabularnewline
44 & 0.413600009961027 & 0.827200019922053 & 0.586399990038973 \tabularnewline
45 & 0.367850711102947 & 0.735701422205894 & 0.632149288897053 \tabularnewline
46 & 0.554732145437648 & 0.890535709124704 & 0.445267854562352 \tabularnewline
47 & 0.514210114887598 & 0.971579770224804 & 0.485789885112402 \tabularnewline
48 & 0.477761851449873 & 0.955523702899746 & 0.522238148550127 \tabularnewline
49 & 0.399128694765867 & 0.798257389531733 & 0.600871305234133 \tabularnewline
50 & 0.3382228611141 & 0.6764457222282 & 0.6617771388859 \tabularnewline
51 & 0.254036744574418 & 0.508073489148835 & 0.745963255425582 \tabularnewline
52 & 0.252887259762176 & 0.505774519524352 & 0.747112740237824 \tabularnewline
53 & 0.174935562141397 & 0.349871124282795 & 0.825064437858603 \tabularnewline
54 & 0.150318755398436 & 0.300637510796873 & 0.849681244601564 \tabularnewline
55 & 0.249077853181075 & 0.498155706362151 & 0.750922146818925 \tabularnewline
56 & 0.722328815731794 & 0.555342368536411 & 0.277671184268206 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57756&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]0.48852038055793[/C][C]0.97704076111586[/C][C]0.51147961944207[/C][/ROW]
[ROW][C]6[/C][C]0.428458910274713[/C][C]0.856917820549426[/C][C]0.571541089725287[/C][/ROW]
[ROW][C]7[/C][C]0.71557030272188[/C][C]0.56885939455624[/C][C]0.28442969727812[/C][/ROW]
[ROW][C]8[/C][C]0.609286736705344[/C][C]0.781426526589311[/C][C]0.390713263294656[/C][/ROW]
[ROW][C]9[/C][C]0.70266326295086[/C][C]0.59467347409828[/C][C]0.29733673704914[/C][/ROW]
[ROW][C]10[/C][C]0.625207867714216[/C][C]0.749584264571568[/C][C]0.374792132285784[/C][/ROW]
[ROW][C]11[/C][C]0.521607312123254[/C][C]0.956785375753493[/C][C]0.478392687876746[/C][/ROW]
[ROW][C]12[/C][C]0.421190775049421[/C][C]0.842381550098842[/C][C]0.578809224950579[/C][/ROW]
[ROW][C]13[/C][C]0.440110285932732[/C][C]0.880220571865464[/C][C]0.559889714067268[/C][/ROW]
[ROW][C]14[/C][C]0.408454249576488[/C][C]0.816908499152976[/C][C]0.591545750423512[/C][/ROW]
[ROW][C]15[/C][C]0.321280151393456[/C][C]0.642560302786911[/C][C]0.678719848606544[/C][/ROW]
[ROW][C]16[/C][C]0.244119043610301[/C][C]0.488238087220602[/C][C]0.755880956389699[/C][/ROW]
[ROW][C]17[/C][C]0.198337163713323[/C][C]0.396674327426646[/C][C]0.801662836286677[/C][/ROW]
[ROW][C]18[/C][C]0.216273555822111[/C][C]0.432547111644221[/C][C]0.78372644417789[/C][/ROW]
[ROW][C]19[/C][C]0.646423846269238[/C][C]0.707152307461524[/C][C]0.353576153730762[/C][/ROW]
[ROW][C]20[/C][C]0.609420193752477[/C][C]0.781159612495047[/C][C]0.390579806247523[/C][/ROW]
[ROW][C]21[/C][C]0.65424185088829[/C][C]0.69151629822342[/C][C]0.34575814911171[/C][/ROW]
[ROW][C]22[/C][C]0.587654158742085[/C][C]0.82469168251583[/C][C]0.412345841257915[/C][/ROW]
[ROW][C]23[/C][C]0.539067037226234[/C][C]0.921865925547532[/C][C]0.460932962773766[/C][/ROW]
[ROW][C]24[/C][C]0.464522837943202[/C][C]0.929045675886404[/C][C]0.535477162056798[/C][/ROW]
[ROW][C]25[/C][C]0.415326426731082[/C][C]0.830652853462164[/C][C]0.584673573268918[/C][/ROW]
[ROW][C]26[/C][C]0.354391153321591[/C][C]0.708782306643182[/C][C]0.64560884667841[/C][/ROW]
[ROW][C]27[/C][C]0.397008316296813[/C][C]0.794016632593626[/C][C]0.602991683703187[/C][/ROW]
[ROW][C]28[/C][C]0.345435367254843[/C][C]0.690870734509686[/C][C]0.654564632745157[/C][/ROW]
[ROW][C]29[/C][C]0.293850043565377[/C][C]0.587700087130755[/C][C]0.706149956434623[/C][/ROW]
[ROW][C]30[/C][C]0.288189512860958[/C][C]0.576379025721917[/C][C]0.711810487139042[/C][/ROW]
[ROW][C]31[/C][C]0.612133271886826[/C][C]0.775733456226348[/C][C]0.387866728113174[/C][/ROW]
[ROW][C]32[/C][C]0.567073623823013[/C][C]0.865852752353973[/C][C]0.432926376176987[/C][/ROW]
[ROW][C]33[/C][C]0.58076415191958[/C][C]0.83847169616084[/C][C]0.41923584808042[/C][/ROW]
[ROW][C]34[/C][C]0.626739926317229[/C][C]0.746520147365542[/C][C]0.373260073682771[/C][/ROW]
[ROW][C]35[/C][C]0.608536122972555[/C][C]0.78292775405489[/C][C]0.391463877027445[/C][/ROW]
[ROW][C]36[/C][C]0.544258707418428[/C][C]0.911482585163144[/C][C]0.455741292581572[/C][/ROW]
[ROW][C]37[/C][C]0.468184108557091[/C][C]0.936368217114181[/C][C]0.531815891442909[/C][/ROW]
[ROW][C]38[/C][C]0.391673603726995[/C][C]0.783347207453991[/C][C]0.608326396273005[/C][/ROW]
[ROW][C]39[/C][C]0.482937244043709[/C][C]0.965874488087417[/C][C]0.517062755956291[/C][/ROW]
[ROW][C]40[/C][C]0.404306075572635[/C][C]0.80861215114527[/C][C]0.595693924427365[/C][/ROW]
[ROW][C]41[/C][C]0.340321362029421[/C][C]0.680642724058842[/C][C]0.659678637970579[/C][/ROW]
[ROW][C]42[/C][C]0.387717356944843[/C][C]0.775434713889685[/C][C]0.612282643055157[/C][/ROW]
[ROW][C]43[/C][C]0.49536785703022[/C][C]0.99073571406044[/C][C]0.50463214296978[/C][/ROW]
[ROW][C]44[/C][C]0.413600009961027[/C][C]0.827200019922053[/C][C]0.586399990038973[/C][/ROW]
[ROW][C]45[/C][C]0.367850711102947[/C][C]0.735701422205894[/C][C]0.632149288897053[/C][/ROW]
[ROW][C]46[/C][C]0.554732145437648[/C][C]0.890535709124704[/C][C]0.445267854562352[/C][/ROW]
[ROW][C]47[/C][C]0.514210114887598[/C][C]0.971579770224804[/C][C]0.485789885112402[/C][/ROW]
[ROW][C]48[/C][C]0.477761851449873[/C][C]0.955523702899746[/C][C]0.522238148550127[/C][/ROW]
[ROW][C]49[/C][C]0.399128694765867[/C][C]0.798257389531733[/C][C]0.600871305234133[/C][/ROW]
[ROW][C]50[/C][C]0.3382228611141[/C][C]0.6764457222282[/C][C]0.6617771388859[/C][/ROW]
[ROW][C]51[/C][C]0.254036744574418[/C][C]0.508073489148835[/C][C]0.745963255425582[/C][/ROW]
[ROW][C]52[/C][C]0.252887259762176[/C][C]0.505774519524352[/C][C]0.747112740237824[/C][/ROW]
[ROW][C]53[/C][C]0.174935562141397[/C][C]0.349871124282795[/C][C]0.825064437858603[/C][/ROW]
[ROW][C]54[/C][C]0.150318755398436[/C][C]0.300637510796873[/C][C]0.849681244601564[/C][/ROW]
[ROW][C]55[/C][C]0.249077853181075[/C][C]0.498155706362151[/C][C]0.750922146818925[/C][/ROW]
[ROW][C]56[/C][C]0.722328815731794[/C][C]0.555342368536411[/C][C]0.277671184268206[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57756&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57756&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
50.488520380557930.977040761115860.51147961944207
60.4284589102747130.8569178205494260.571541089725287
70.715570302721880.568859394556240.28442969727812
80.6092867367053440.7814265265893110.390713263294656
90.702663262950860.594673474098280.29733673704914
100.6252078677142160.7495842645715680.374792132285784
110.5216073121232540.9567853757534930.478392687876746
120.4211907750494210.8423815500988420.578809224950579
130.4401102859327320.8802205718654640.559889714067268
140.4084542495764880.8169084991529760.591545750423512
150.3212801513934560.6425603027869110.678719848606544
160.2441190436103010.4882380872206020.755880956389699
170.1983371637133230.3966743274266460.801662836286677
180.2162735558221110.4325471116442210.78372644417789
190.6464238462692380.7071523074615240.353576153730762
200.6094201937524770.7811596124950470.390579806247523
210.654241850888290.691516298223420.34575814911171
220.5876541587420850.824691682515830.412345841257915
230.5390670372262340.9218659255475320.460932962773766
240.4645228379432020.9290456758864040.535477162056798
250.4153264267310820.8306528534621640.584673573268918
260.3543911533215910.7087823066431820.64560884667841
270.3970083162968130.7940166325936260.602991683703187
280.3454353672548430.6908707345096860.654564632745157
290.2938500435653770.5877000871307550.706149956434623
300.2881895128609580.5763790257219170.711810487139042
310.6121332718868260.7757334562263480.387866728113174
320.5670736238230130.8658527523539730.432926376176987
330.580764151919580.838471696160840.41923584808042
340.6267399263172290.7465201473655420.373260073682771
350.6085361229725550.782927754054890.391463877027445
360.5442587074184280.9114825851631440.455741292581572
370.4681841085570910.9363682171141810.531815891442909
380.3916736037269950.7833472074539910.608326396273005
390.4829372440437090.9658744880874170.517062755956291
400.4043060755726350.808612151145270.595693924427365
410.3403213620294210.6806427240588420.659678637970579
420.3877173569448430.7754347138896850.612282643055157
430.495367857030220.990735714060440.50463214296978
440.4136000099610270.8272000199220530.586399990038973
450.3678507111029470.7357014222058940.632149288897053
460.5547321454376480.8905357091247040.445267854562352
470.5142101148875980.9715797702248040.485789885112402
480.4777618514498730.9555237028997460.522238148550127
490.3991286947658670.7982573895317330.600871305234133
500.33822286111410.67644572222820.6617771388859
510.2540367445744180.5080734891488350.745963255425582
520.2528872597621760.5057745195243520.747112740237824
530.1749355621413970.3498711242827950.825064437858603
540.1503187553984360.3006375107968730.849681244601564
550.2490778531810750.4981557063621510.750922146818925
560.7223288157317940.5553423685364110.277671184268206







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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57756&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57756&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57756&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 level00OK
10% type I error level00OK



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')
}