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multiple regression met 4 maanden minder, index van totale industriële prod...

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 08:39:29 -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/t1258645570ugteo28lcufbaj6.htm/, Retrieved Thu, 28 Mar 2024 16:22:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57778, Retrieved Thu, 28 Mar 2024 16:22:43 +0000
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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]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:10:54] [b98453cac15ba1066b407e146608df68]
-   PD      [Multiple Regression] [multiple regressi...] [2009-11-19 15:39:29] [8f072ead2c7c0b3cf3fdae49bab9dd9b] [Current]
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Dataseries X:
97.4	134.6	102.9	112.7	97	95.1
111.4	131.8	97.4	102.9	112.7	97
87.4	135.9	111.4	97.4	102.9	112.7
96.8	142.7	87.4	111.4	97.4	102.9
114.1	141.7	96.8	87.4	111.4	97.4
110.3	153.4	114.1	96.8	87.4	111.4
103.9	145	110.3	114.1	96.8	87.4
101.6	137.7	103.9	110.3	114.1	96.8
94.6	148.3	101.6	103.9	110.3	114.1
95.9	152.2	94.6	101.6	103.9	110.3
104.7	169.4	95.9	94.6	101.6	103.9
102.8	168.6	104.7	95.9	94.6	101.6
98.1	161.1	102.8	104.7	95.9	94.6
113.9	174.1	98.1	102.8	104.7	95.9
80.9	179	113.9	98.1	102.8	104.7
95.7	190.6	80.9	113.9	98.1	102.8
113.2	190	95.7	80.9	113.9	98.1
105.9	181.6	113.2	95.7	80.9	113.9
108.8	174.8	105.9	113.2	95.7	80.9
102.3	180.5	108.8	105.9	113.2	95.7
99	196.8	102.3	108.8	105.9	113.2
100.7	193.8	99	102.3	108.8	105.9
115.5	197	100.7	99	102.3	108.8
100.7	216.3	115.5	100.7	99	102.3
109.9	221.4	100.7	115.5	100.7	99
114.6	217.9	109.9	100.7	115.5	100.7
85.4	229.7	114.6	109.9	100.7	115.5
100.5	227.4	85.4	114.6	109.9	100.7
114.8	204.2	100.5	85.4	114.6	109.9
116.5	196.6	114.8	100.5	85.4	114.6
112.9	198.8	116.5	114.8	100.5	85.4
102	207.5	112.9	116.5	114.8	100.5
106	190.7	102	112.9	116.5	114.8
105.3	201.6	106	102	112.9	116.5
118.8	210.5	105.3	106	102	112.9
106.1	223.5	118.8	105.3	106	102
109.3	223.8	106.1	118.8	105.3	106
117.2	231.2	109.3	106.1	118.8	105.3
92.5	244	117.2	109.3	106.1	118.8
104.2	234.7	92.5	117.2	109.3	106.1
112.5	250.2	104.2	92.5	117.2	109.3
122.4	265.7	112.5	104.2	92.5	117.2
113.3	287.6	122.4	112.5	104.2	92.5
100	283.3	113.3	122.4	112.5	104.2
110.7	295.4	100	113.3	122.4	112.5
112.8	312.3	110.7	100	113.3	122.4
109.8	333.8	112.8	110.7	100	113.3
117.3	347.7	109.8	112.8	110.7	100
109.1	383.2	117.3	109.8	112.8	110.7
115.9	407.1	109.1	117.3	109.8	112.8
96	413.6	115.9	109.1	117.3	109.8
99.8	362.7	96	115.9	109.1	117.3
116.8	321.9	99.8	96	115.9	109.1
115.7	239.4	116.8	99.8	96	115.9
99.4	191	115.7	116.8	99.8	96
94.3	159.7	99.4	115.7	116.8	99.8
91	163.4	94.3	99.4	115.7	116.8




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=57778&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=57778&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57778&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
tot.ind.prod.index[t] = + 181.261496740705 + 0.0723003449900399prijsindex.grondst.incl.energie[t] -0.0344610887961219`y(t-1)`[t] -0.405354806386297`y(t-2)`[t] -0.112859202625963`y(t-3)`[t] -0.31403889358322`y(t-4)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
tot.ind.prod.index[t] =  +  181.261496740705 +  0.0723003449900399prijsindex.grondst.incl.energie[t] -0.0344610887961219`y(t-1)`[t] -0.405354806386297`y(t-2)`[t] -0.112859202625963`y(t-3)`[t] -0.31403889358322`y(t-4)`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57778&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]tot.ind.prod.index[t] =  +  181.261496740705 +  0.0723003449900399prijsindex.grondst.incl.energie[t] -0.0344610887961219`y(t-1)`[t] -0.405354806386297`y(t-2)`[t] -0.112859202625963`y(t-3)`[t] -0.31403889358322`y(t-4)`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57778&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57778&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] = + 181.261496740705 + 0.0723003449900399prijsindex.grondst.incl.energie[t] -0.0344610887961219`y(t-1)`[t] -0.405354806386297`y(t-2)`[t] -0.112859202625963`y(t-3)`[t] -0.31403889358322`y(t-4)`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)181.26149674070533.2480055.45181e-061e-06
prijsindex.grondst.incl.energie0.07230034499003990.0209013.45920.0011030.000552
`y(t-1)`-0.03446108879612190.13934-0.24730.8056560.402828
`y(t-2)`-0.4053548063862970.139292-2.91010.0053430.002672
`y(t-3)`-0.1128592026259630.139774-0.80740.4231630.211581
`y(t-4)`-0.314038893583220.142256-2.20760.03180.0159

\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) & 181.261496740705 & 33.248005 & 5.4518 & 1e-06 & 1e-06 \tabularnewline
prijsindex.grondst.incl.energie & 0.0723003449900399 & 0.020901 & 3.4592 & 0.001103 & 0.000552 \tabularnewline
`y(t-1)` & -0.0344610887961219 & 0.13934 & -0.2473 & 0.805656 & 0.402828 \tabularnewline
`y(t-2)` & -0.405354806386297 & 0.139292 & -2.9101 & 0.005343 & 0.002672 \tabularnewline
`y(t-3)` & -0.112859202625963 & 0.139774 & -0.8074 & 0.423163 & 0.211581 \tabularnewline
`y(t-4)` & -0.31403889358322 & 0.142256 & -2.2076 & 0.0318 & 0.0159 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57778&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]181.261496740705[/C][C]33.248005[/C][C]5.4518[/C][C]1e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]prijsindex.grondst.incl.energie[/C][C]0.0723003449900399[/C][C]0.020901[/C][C]3.4592[/C][C]0.001103[/C][C]0.000552[/C][/ROW]
[ROW][C]`y(t-1)`[/C][C]-0.0344610887961219[/C][C]0.13934[/C][C]-0.2473[/C][C]0.805656[/C][C]0.402828[/C][/ROW]
[ROW][C]`y(t-2)`[/C][C]-0.405354806386297[/C][C]0.139292[/C][C]-2.9101[/C][C]0.005343[/C][C]0.002672[/C][/ROW]
[ROW][C]`y(t-3)`[/C][C]-0.112859202625963[/C][C]0.139774[/C][C]-0.8074[/C][C]0.423163[/C][C]0.211581[/C][/ROW]
[ROW][C]`y(t-4)`[/C][C]-0.31403889358322[/C][C]0.142256[/C][C]-2.2076[/C][C]0.0318[/C][C]0.0159[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57778&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57778&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)181.26149674070533.2480055.45181e-061e-06
prijsindex.grondst.incl.energie0.07230034499003990.0209013.45920.0011030.000552
`y(t-1)`-0.03446108879612190.13934-0.24730.8056560.402828
`y(t-2)`-0.4053548063862970.139292-2.91010.0053430.002672
`y(t-3)`-0.1128592026259630.139774-0.80740.4231630.211581
`y(t-4)`-0.314038893583220.142256-2.20760.03180.0159







Multiple Linear Regression - Regression Statistics
Multiple R0.491238995726365
R-squared0.241315750922248
Adjusted R-squared0.166934942189135
F-TEST (value)3.24432814098751
F-TEST (DF numerator)5
F-TEST (DF denominator)51
p-value0.0128063031585952
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8.40592598582137
Sum Squared Residuals3603.63917563446

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.491238995726365 \tabularnewline
R-squared & 0.241315750922248 \tabularnewline
Adjusted R-squared & 0.166934942189135 \tabularnewline
F-TEST (value) & 3.24432814098751 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 51 \tabularnewline
p-value & 0.0128063031585952 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 8.40592598582137 \tabularnewline
Sum Squared Residuals & 3603.63917563446 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57778&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.491238995726365[/C][/ROW]
[ROW][C]R-squared[/C][C]0.241315750922248[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.166934942189135[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.24432814098751[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]51[/C][/ROW]
[ROW][C]p-value[/C][C]0.0128063031585952[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]8.40592598582137[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]3603.63917563446[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57778&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57778&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 R0.491238995726365
R-squared0.241315750922248
Adjusted R-squared0.166934942189135
F-TEST (value)3.24432814098751
F-TEST (DF numerator)5
F-TEST (DF denominator)51
p-value0.0128063031585952
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8.40592598582137
Sum Squared Residuals3603.63917563446







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
197.4100.951149025025-3.55114902502536
2111.4102.5421577709828.8578422290183
387.4100.761194933898-13.3611949338977
496.8100.103242893087-3.30324289308704
5114.1109.5827087446294.51729125537117
6110.3104.3341871176665.96581288233378
7103.9103.3212351480050.57876485199455
8101.699.64991205702991.95008794297015
994.698.0858190900166-3.4858190900166
1095.9101.456980804161-5.55698080416148
11104.7107.762656052232-3.06265605223161
12102.8108.386900820155-5.58690082015467
1398.1106.394557296911-8.29455729691138
14113.9106.8651914864917.03480851350903
1580.9106.031035785436-25.1310357854360
1695.7102.729441926839-7.02944192683917
17113.2115.245553614761-2.04555361476126
18105.9106.798449696437-0.898449696437474
19108.8108.1576314763390.642368523661264
20102.3104.806084700907-2.50608470090717
2199100.361240004363-1.36124000436252
22100.7104.858059039473-4.15805903947275
23115.5106.1913791792409.30862082076033
24100.7108.801336729465-8.10133672946474
25109.9104.5253091729405.37469082706016
26114.6107.7502847651126.84971523488814
2785.4101.734738073731-16.3347380737312
28100.5104.279014443958-3.77901444395809
29114.8110.4980482725404.30195172746007
30116.5105.15442042123511.3455795787650
31112.9106.9240853309145.97591466908639
32102100.6321811904781.36781880952160
3310696.569821742819.43017825719006
34105.3101.5108455479903.78915445201048
35118.8102.91772748053615.8822725194644
36106.1106.647742760682-0.547742760682093
37109.3100.4576446731808.84235532681986
38117.2104.72662577312312.4733742268774
3992.5101.276479017046-8.77647901704582
40104.2101.8801162315552.31988376844527
41112.5110.7133283975161.78667160248416
42122.4107.11202051868815.2879794813122
43113.3111.4260964026641.87390359733640
44100102.804801807308-2.80480180730762
45110.7104.1028682780536.59713172194693
46112.8108.2652630806264.53473691937363
47109.8109.7688371096400.0311628903603979
48117.3112.9950738945374.30492610546267
49109.1112.922121908017-3.82212190801674
50115.9111.5716159648634.32838403513721
5196115.226814876907-19.226814876907
5299.8108.046244060189-8.24624406018872
53116.8114.8396748437831.96032515621701
54115.7106.8591432641938.84085673580655
5599.4102.327191068112-2.92719106811164
5694.397.9598420640675-3.65984206406751
579199.7958721694613-8.79587216946133

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 97.4 & 100.951149025025 & -3.55114902502536 \tabularnewline
2 & 111.4 & 102.542157770982 & 8.8578422290183 \tabularnewline
3 & 87.4 & 100.761194933898 & -13.3611949338977 \tabularnewline
4 & 96.8 & 100.103242893087 & -3.30324289308704 \tabularnewline
5 & 114.1 & 109.582708744629 & 4.51729125537117 \tabularnewline
6 & 110.3 & 104.334187117666 & 5.96581288233378 \tabularnewline
7 & 103.9 & 103.321235148005 & 0.57876485199455 \tabularnewline
8 & 101.6 & 99.6499120570299 & 1.95008794297015 \tabularnewline
9 & 94.6 & 98.0858190900166 & -3.4858190900166 \tabularnewline
10 & 95.9 & 101.456980804161 & -5.55698080416148 \tabularnewline
11 & 104.7 & 107.762656052232 & -3.06265605223161 \tabularnewline
12 & 102.8 & 108.386900820155 & -5.58690082015467 \tabularnewline
13 & 98.1 & 106.394557296911 & -8.29455729691138 \tabularnewline
14 & 113.9 & 106.865191486491 & 7.03480851350903 \tabularnewline
15 & 80.9 & 106.031035785436 & -25.1310357854360 \tabularnewline
16 & 95.7 & 102.729441926839 & -7.02944192683917 \tabularnewline
17 & 113.2 & 115.245553614761 & -2.04555361476126 \tabularnewline
18 & 105.9 & 106.798449696437 & -0.898449696437474 \tabularnewline
19 & 108.8 & 108.157631476339 & 0.642368523661264 \tabularnewline
20 & 102.3 & 104.806084700907 & -2.50608470090717 \tabularnewline
21 & 99 & 100.361240004363 & -1.36124000436252 \tabularnewline
22 & 100.7 & 104.858059039473 & -4.15805903947275 \tabularnewline
23 & 115.5 & 106.191379179240 & 9.30862082076033 \tabularnewline
24 & 100.7 & 108.801336729465 & -8.10133672946474 \tabularnewline
25 & 109.9 & 104.525309172940 & 5.37469082706016 \tabularnewline
26 & 114.6 & 107.750284765112 & 6.84971523488814 \tabularnewline
27 & 85.4 & 101.734738073731 & -16.3347380737312 \tabularnewline
28 & 100.5 & 104.279014443958 & -3.77901444395809 \tabularnewline
29 & 114.8 & 110.498048272540 & 4.30195172746007 \tabularnewline
30 & 116.5 & 105.154420421235 & 11.3455795787650 \tabularnewline
31 & 112.9 & 106.924085330914 & 5.97591466908639 \tabularnewline
32 & 102 & 100.632181190478 & 1.36781880952160 \tabularnewline
33 & 106 & 96.56982174281 & 9.43017825719006 \tabularnewline
34 & 105.3 & 101.510845547990 & 3.78915445201048 \tabularnewline
35 & 118.8 & 102.917727480536 & 15.8822725194644 \tabularnewline
36 & 106.1 & 106.647742760682 & -0.547742760682093 \tabularnewline
37 & 109.3 & 100.457644673180 & 8.84235532681986 \tabularnewline
38 & 117.2 & 104.726625773123 & 12.4733742268774 \tabularnewline
39 & 92.5 & 101.276479017046 & -8.77647901704582 \tabularnewline
40 & 104.2 & 101.880116231555 & 2.31988376844527 \tabularnewline
41 & 112.5 & 110.713328397516 & 1.78667160248416 \tabularnewline
42 & 122.4 & 107.112020518688 & 15.2879794813122 \tabularnewline
43 & 113.3 & 111.426096402664 & 1.87390359733640 \tabularnewline
44 & 100 & 102.804801807308 & -2.80480180730762 \tabularnewline
45 & 110.7 & 104.102868278053 & 6.59713172194693 \tabularnewline
46 & 112.8 & 108.265263080626 & 4.53473691937363 \tabularnewline
47 & 109.8 & 109.768837109640 & 0.0311628903603979 \tabularnewline
48 & 117.3 & 112.995073894537 & 4.30492610546267 \tabularnewline
49 & 109.1 & 112.922121908017 & -3.82212190801674 \tabularnewline
50 & 115.9 & 111.571615964863 & 4.32838403513721 \tabularnewline
51 & 96 & 115.226814876907 & -19.226814876907 \tabularnewline
52 & 99.8 & 108.046244060189 & -8.24624406018872 \tabularnewline
53 & 116.8 & 114.839674843783 & 1.96032515621701 \tabularnewline
54 & 115.7 & 106.859143264193 & 8.84085673580655 \tabularnewline
55 & 99.4 & 102.327191068112 & -2.92719106811164 \tabularnewline
56 & 94.3 & 97.9598420640675 & -3.65984206406751 \tabularnewline
57 & 91 & 99.7958721694613 & -8.79587216946133 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57778&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]97.4[/C][C]100.951149025025[/C][C]-3.55114902502536[/C][/ROW]
[ROW][C]2[/C][C]111.4[/C][C]102.542157770982[/C][C]8.8578422290183[/C][/ROW]
[ROW][C]3[/C][C]87.4[/C][C]100.761194933898[/C][C]-13.3611949338977[/C][/ROW]
[ROW][C]4[/C][C]96.8[/C][C]100.103242893087[/C][C]-3.30324289308704[/C][/ROW]
[ROW][C]5[/C][C]114.1[/C][C]109.582708744629[/C][C]4.51729125537117[/C][/ROW]
[ROW][C]6[/C][C]110.3[/C][C]104.334187117666[/C][C]5.96581288233378[/C][/ROW]
[ROW][C]7[/C][C]103.9[/C][C]103.321235148005[/C][C]0.57876485199455[/C][/ROW]
[ROW][C]8[/C][C]101.6[/C][C]99.6499120570299[/C][C]1.95008794297015[/C][/ROW]
[ROW][C]9[/C][C]94.6[/C][C]98.0858190900166[/C][C]-3.4858190900166[/C][/ROW]
[ROW][C]10[/C][C]95.9[/C][C]101.456980804161[/C][C]-5.55698080416148[/C][/ROW]
[ROW][C]11[/C][C]104.7[/C][C]107.762656052232[/C][C]-3.06265605223161[/C][/ROW]
[ROW][C]12[/C][C]102.8[/C][C]108.386900820155[/C][C]-5.58690082015467[/C][/ROW]
[ROW][C]13[/C][C]98.1[/C][C]106.394557296911[/C][C]-8.29455729691138[/C][/ROW]
[ROW][C]14[/C][C]113.9[/C][C]106.865191486491[/C][C]7.03480851350903[/C][/ROW]
[ROW][C]15[/C][C]80.9[/C][C]106.031035785436[/C][C]-25.1310357854360[/C][/ROW]
[ROW][C]16[/C][C]95.7[/C][C]102.729441926839[/C][C]-7.02944192683917[/C][/ROW]
[ROW][C]17[/C][C]113.2[/C][C]115.245553614761[/C][C]-2.04555361476126[/C][/ROW]
[ROW][C]18[/C][C]105.9[/C][C]106.798449696437[/C][C]-0.898449696437474[/C][/ROW]
[ROW][C]19[/C][C]108.8[/C][C]108.157631476339[/C][C]0.642368523661264[/C][/ROW]
[ROW][C]20[/C][C]102.3[/C][C]104.806084700907[/C][C]-2.50608470090717[/C][/ROW]
[ROW][C]21[/C][C]99[/C][C]100.361240004363[/C][C]-1.36124000436252[/C][/ROW]
[ROW][C]22[/C][C]100.7[/C][C]104.858059039473[/C][C]-4.15805903947275[/C][/ROW]
[ROW][C]23[/C][C]115.5[/C][C]106.191379179240[/C][C]9.30862082076033[/C][/ROW]
[ROW][C]24[/C][C]100.7[/C][C]108.801336729465[/C][C]-8.10133672946474[/C][/ROW]
[ROW][C]25[/C][C]109.9[/C][C]104.525309172940[/C][C]5.37469082706016[/C][/ROW]
[ROW][C]26[/C][C]114.6[/C][C]107.750284765112[/C][C]6.84971523488814[/C][/ROW]
[ROW][C]27[/C][C]85.4[/C][C]101.734738073731[/C][C]-16.3347380737312[/C][/ROW]
[ROW][C]28[/C][C]100.5[/C][C]104.279014443958[/C][C]-3.77901444395809[/C][/ROW]
[ROW][C]29[/C][C]114.8[/C][C]110.498048272540[/C][C]4.30195172746007[/C][/ROW]
[ROW][C]30[/C][C]116.5[/C][C]105.154420421235[/C][C]11.3455795787650[/C][/ROW]
[ROW][C]31[/C][C]112.9[/C][C]106.924085330914[/C][C]5.97591466908639[/C][/ROW]
[ROW][C]32[/C][C]102[/C][C]100.632181190478[/C][C]1.36781880952160[/C][/ROW]
[ROW][C]33[/C][C]106[/C][C]96.56982174281[/C][C]9.43017825719006[/C][/ROW]
[ROW][C]34[/C][C]105.3[/C][C]101.510845547990[/C][C]3.78915445201048[/C][/ROW]
[ROW][C]35[/C][C]118.8[/C][C]102.917727480536[/C][C]15.8822725194644[/C][/ROW]
[ROW][C]36[/C][C]106.1[/C][C]106.647742760682[/C][C]-0.547742760682093[/C][/ROW]
[ROW][C]37[/C][C]109.3[/C][C]100.457644673180[/C][C]8.84235532681986[/C][/ROW]
[ROW][C]38[/C][C]117.2[/C][C]104.726625773123[/C][C]12.4733742268774[/C][/ROW]
[ROW][C]39[/C][C]92.5[/C][C]101.276479017046[/C][C]-8.77647901704582[/C][/ROW]
[ROW][C]40[/C][C]104.2[/C][C]101.880116231555[/C][C]2.31988376844527[/C][/ROW]
[ROW][C]41[/C][C]112.5[/C][C]110.713328397516[/C][C]1.78667160248416[/C][/ROW]
[ROW][C]42[/C][C]122.4[/C][C]107.112020518688[/C][C]15.2879794813122[/C][/ROW]
[ROW][C]43[/C][C]113.3[/C][C]111.426096402664[/C][C]1.87390359733640[/C][/ROW]
[ROW][C]44[/C][C]100[/C][C]102.804801807308[/C][C]-2.80480180730762[/C][/ROW]
[ROW][C]45[/C][C]110.7[/C][C]104.102868278053[/C][C]6.59713172194693[/C][/ROW]
[ROW][C]46[/C][C]112.8[/C][C]108.265263080626[/C][C]4.53473691937363[/C][/ROW]
[ROW][C]47[/C][C]109.8[/C][C]109.768837109640[/C][C]0.0311628903603979[/C][/ROW]
[ROW][C]48[/C][C]117.3[/C][C]112.995073894537[/C][C]4.30492610546267[/C][/ROW]
[ROW][C]49[/C][C]109.1[/C][C]112.922121908017[/C][C]-3.82212190801674[/C][/ROW]
[ROW][C]50[/C][C]115.9[/C][C]111.571615964863[/C][C]4.32838403513721[/C][/ROW]
[ROW][C]51[/C][C]96[/C][C]115.226814876907[/C][C]-19.226814876907[/C][/ROW]
[ROW][C]52[/C][C]99.8[/C][C]108.046244060189[/C][C]-8.24624406018872[/C][/ROW]
[ROW][C]53[/C][C]116.8[/C][C]114.839674843783[/C][C]1.96032515621701[/C][/ROW]
[ROW][C]54[/C][C]115.7[/C][C]106.859143264193[/C][C]8.84085673580655[/C][/ROW]
[ROW][C]55[/C][C]99.4[/C][C]102.327191068112[/C][C]-2.92719106811164[/C][/ROW]
[ROW][C]56[/C][C]94.3[/C][C]97.9598420640675[/C][C]-3.65984206406751[/C][/ROW]
[ROW][C]57[/C][C]91[/C][C]99.7958721694613[/C][C]-8.79587216946133[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57778&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57778&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
197.4100.951149025025-3.55114902502536
2111.4102.5421577709828.8578422290183
387.4100.761194933898-13.3611949338977
496.8100.103242893087-3.30324289308704
5114.1109.5827087446294.51729125537117
6110.3104.3341871176665.96581288233378
7103.9103.3212351480050.57876485199455
8101.699.64991205702991.95008794297015
994.698.0858190900166-3.4858190900166
1095.9101.456980804161-5.55698080416148
11104.7107.762656052232-3.06265605223161
12102.8108.386900820155-5.58690082015467
1398.1106.394557296911-8.29455729691138
14113.9106.8651914864917.03480851350903
1580.9106.031035785436-25.1310357854360
1695.7102.729441926839-7.02944192683917
17113.2115.245553614761-2.04555361476126
18105.9106.798449696437-0.898449696437474
19108.8108.1576314763390.642368523661264
20102.3104.806084700907-2.50608470090717
2199100.361240004363-1.36124000436252
22100.7104.858059039473-4.15805903947275
23115.5106.1913791792409.30862082076033
24100.7108.801336729465-8.10133672946474
25109.9104.5253091729405.37469082706016
26114.6107.7502847651126.84971523488814
2785.4101.734738073731-16.3347380737312
28100.5104.279014443958-3.77901444395809
29114.8110.4980482725404.30195172746007
30116.5105.15442042123511.3455795787650
31112.9106.9240853309145.97591466908639
32102100.6321811904781.36781880952160
3310696.569821742819.43017825719006
34105.3101.5108455479903.78915445201048
35118.8102.91772748053615.8822725194644
36106.1106.647742760682-0.547742760682093
37109.3100.4576446731808.84235532681986
38117.2104.72662577312312.4733742268774
3992.5101.276479017046-8.77647901704582
40104.2101.8801162315552.31988376844527
41112.5110.7133283975161.78667160248416
42122.4107.11202051868815.2879794813122
43113.3111.4260964026641.87390359733640
44100102.804801807308-2.80480180730762
45110.7104.1028682780536.59713172194693
46112.8108.2652630806264.53473691937363
47109.8109.7688371096400.0311628903603979
48117.3112.9950738945374.30492610546267
49109.1112.922121908017-3.82212190801674
50115.9111.5716159648634.32838403513721
5196115.226814876907-19.226814876907
5299.8108.046244060189-8.24624406018872
53116.8114.8396748437831.96032515621701
54115.7106.8591432641938.84085673580655
5599.4102.327191068112-2.92719106811164
5694.397.9598420640675-3.65984206406751
579199.7958721694613-8.79587216946133







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.533466117115880.9330677657682410.466533882884121
100.4312993872428890.8625987744857770.568700612757111
110.3624464184824820.7248928369649630.637553581517518
120.2846606541542330.5693213083084660.715339345845767
130.2561332413166170.5122664826332330.743866758683383
140.2522026618773890.5044053237547790.74779733812261
150.6804919267396140.6390161465207720.319508073260386
160.6087996051732750.782400789653450.391200394826725
170.5176354446558320.9647291106883360.482364555344168
180.5514993328544220.8970013342911550.448500667145578
190.4690076077022570.9380152154045140.530992392297743
200.4297271553678010.8594543107356020.570272844632199
210.439338267360170.878676534720340.56066173263983
220.3825402978842160.7650805957684310.617459702115784
230.4711456124772710.9422912249545430.528854387522729
240.4742451890696360.9484903781392730.525754810930364
250.4430419289161650.886083857832330.556958071083835
260.4210982409910040.8421964819820080.578901759008996
270.6481066961945570.7037866076108870.351893303805443
280.6109827327234350.778034534553130.389017267276565
290.5475819885072130.9048360229855750.452418011492787
300.6339568910346850.732086217930630.366043108965315
310.573010105716310.8539797885673810.426989894283691
320.4979969015025670.9959938030051340.502003098497433
330.5456998550495470.9086002899009050.454300144950453
340.4724675180800490.9449350361600970.527532481919951
350.5842276598464520.8315446803070960.415772340153548
360.5031550613354560.9936898773290880.496844938664544
370.4818352350665480.9636704701330960.518164764933452
380.6376206926024850.724758614795030.362379307397515
390.6431078633148670.7137842733702660.356892136685133
400.5508403077834220.8983193844331560.449159692216578
410.454236492652080.908472985304160.54576350734792
420.478536085887570.957072171775140.52146391411243
430.3784234757679370.7568469515358730.621576524232063
440.2871237547537990.5742475095075990.7128762452462
450.4054005732748170.8108011465496350.594599426725183
460.4457127832692650.891425566538530.554287216730735
470.3230046187659480.6460092375318960.676995381234052
480.2183646605040860.4367293210081720.781635339495914

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.53346611711588 & 0.933067765768241 & 0.466533882884121 \tabularnewline
10 & 0.431299387242889 & 0.862598774485777 & 0.568700612757111 \tabularnewline
11 & 0.362446418482482 & 0.724892836964963 & 0.637553581517518 \tabularnewline
12 & 0.284660654154233 & 0.569321308308466 & 0.715339345845767 \tabularnewline
13 & 0.256133241316617 & 0.512266482633233 & 0.743866758683383 \tabularnewline
14 & 0.252202661877389 & 0.504405323754779 & 0.74779733812261 \tabularnewline
15 & 0.680491926739614 & 0.639016146520772 & 0.319508073260386 \tabularnewline
16 & 0.608799605173275 & 0.78240078965345 & 0.391200394826725 \tabularnewline
17 & 0.517635444655832 & 0.964729110688336 & 0.482364555344168 \tabularnewline
18 & 0.551499332854422 & 0.897001334291155 & 0.448500667145578 \tabularnewline
19 & 0.469007607702257 & 0.938015215404514 & 0.530992392297743 \tabularnewline
20 & 0.429727155367801 & 0.859454310735602 & 0.570272844632199 \tabularnewline
21 & 0.43933826736017 & 0.87867653472034 & 0.56066173263983 \tabularnewline
22 & 0.382540297884216 & 0.765080595768431 & 0.617459702115784 \tabularnewline
23 & 0.471145612477271 & 0.942291224954543 & 0.528854387522729 \tabularnewline
24 & 0.474245189069636 & 0.948490378139273 & 0.525754810930364 \tabularnewline
25 & 0.443041928916165 & 0.88608385783233 & 0.556958071083835 \tabularnewline
26 & 0.421098240991004 & 0.842196481982008 & 0.578901759008996 \tabularnewline
27 & 0.648106696194557 & 0.703786607610887 & 0.351893303805443 \tabularnewline
28 & 0.610982732723435 & 0.77803453455313 & 0.389017267276565 \tabularnewline
29 & 0.547581988507213 & 0.904836022985575 & 0.452418011492787 \tabularnewline
30 & 0.633956891034685 & 0.73208621793063 & 0.366043108965315 \tabularnewline
31 & 0.57301010571631 & 0.853979788567381 & 0.426989894283691 \tabularnewline
32 & 0.497996901502567 & 0.995993803005134 & 0.502003098497433 \tabularnewline
33 & 0.545699855049547 & 0.908600289900905 & 0.454300144950453 \tabularnewline
34 & 0.472467518080049 & 0.944935036160097 & 0.527532481919951 \tabularnewline
35 & 0.584227659846452 & 0.831544680307096 & 0.415772340153548 \tabularnewline
36 & 0.503155061335456 & 0.993689877329088 & 0.496844938664544 \tabularnewline
37 & 0.481835235066548 & 0.963670470133096 & 0.518164764933452 \tabularnewline
38 & 0.637620692602485 & 0.72475861479503 & 0.362379307397515 \tabularnewline
39 & 0.643107863314867 & 0.713784273370266 & 0.356892136685133 \tabularnewline
40 & 0.550840307783422 & 0.898319384433156 & 0.449159692216578 \tabularnewline
41 & 0.45423649265208 & 0.90847298530416 & 0.54576350734792 \tabularnewline
42 & 0.47853608588757 & 0.95707217177514 & 0.52146391411243 \tabularnewline
43 & 0.378423475767937 & 0.756846951535873 & 0.621576524232063 \tabularnewline
44 & 0.287123754753799 & 0.574247509507599 & 0.7128762452462 \tabularnewline
45 & 0.405400573274817 & 0.810801146549635 & 0.594599426725183 \tabularnewline
46 & 0.445712783269265 & 0.89142556653853 & 0.554287216730735 \tabularnewline
47 & 0.323004618765948 & 0.646009237531896 & 0.676995381234052 \tabularnewline
48 & 0.218364660504086 & 0.436729321008172 & 0.781635339495914 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57778&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]9[/C][C]0.53346611711588[/C][C]0.933067765768241[/C][C]0.466533882884121[/C][/ROW]
[ROW][C]10[/C][C]0.431299387242889[/C][C]0.862598774485777[/C][C]0.568700612757111[/C][/ROW]
[ROW][C]11[/C][C]0.362446418482482[/C][C]0.724892836964963[/C][C]0.637553581517518[/C][/ROW]
[ROW][C]12[/C][C]0.284660654154233[/C][C]0.569321308308466[/C][C]0.715339345845767[/C][/ROW]
[ROW][C]13[/C][C]0.256133241316617[/C][C]0.512266482633233[/C][C]0.743866758683383[/C][/ROW]
[ROW][C]14[/C][C]0.252202661877389[/C][C]0.504405323754779[/C][C]0.74779733812261[/C][/ROW]
[ROW][C]15[/C][C]0.680491926739614[/C][C]0.639016146520772[/C][C]0.319508073260386[/C][/ROW]
[ROW][C]16[/C][C]0.608799605173275[/C][C]0.78240078965345[/C][C]0.391200394826725[/C][/ROW]
[ROW][C]17[/C][C]0.517635444655832[/C][C]0.964729110688336[/C][C]0.482364555344168[/C][/ROW]
[ROW][C]18[/C][C]0.551499332854422[/C][C]0.897001334291155[/C][C]0.448500667145578[/C][/ROW]
[ROW][C]19[/C][C]0.469007607702257[/C][C]0.938015215404514[/C][C]0.530992392297743[/C][/ROW]
[ROW][C]20[/C][C]0.429727155367801[/C][C]0.859454310735602[/C][C]0.570272844632199[/C][/ROW]
[ROW][C]21[/C][C]0.43933826736017[/C][C]0.87867653472034[/C][C]0.56066173263983[/C][/ROW]
[ROW][C]22[/C][C]0.382540297884216[/C][C]0.765080595768431[/C][C]0.617459702115784[/C][/ROW]
[ROW][C]23[/C][C]0.471145612477271[/C][C]0.942291224954543[/C][C]0.528854387522729[/C][/ROW]
[ROW][C]24[/C][C]0.474245189069636[/C][C]0.948490378139273[/C][C]0.525754810930364[/C][/ROW]
[ROW][C]25[/C][C]0.443041928916165[/C][C]0.88608385783233[/C][C]0.556958071083835[/C][/ROW]
[ROW][C]26[/C][C]0.421098240991004[/C][C]0.842196481982008[/C][C]0.578901759008996[/C][/ROW]
[ROW][C]27[/C][C]0.648106696194557[/C][C]0.703786607610887[/C][C]0.351893303805443[/C][/ROW]
[ROW][C]28[/C][C]0.610982732723435[/C][C]0.77803453455313[/C][C]0.389017267276565[/C][/ROW]
[ROW][C]29[/C][C]0.547581988507213[/C][C]0.904836022985575[/C][C]0.452418011492787[/C][/ROW]
[ROW][C]30[/C][C]0.633956891034685[/C][C]0.73208621793063[/C][C]0.366043108965315[/C][/ROW]
[ROW][C]31[/C][C]0.57301010571631[/C][C]0.853979788567381[/C][C]0.426989894283691[/C][/ROW]
[ROW][C]32[/C][C]0.497996901502567[/C][C]0.995993803005134[/C][C]0.502003098497433[/C][/ROW]
[ROW][C]33[/C][C]0.545699855049547[/C][C]0.908600289900905[/C][C]0.454300144950453[/C][/ROW]
[ROW][C]34[/C][C]0.472467518080049[/C][C]0.944935036160097[/C][C]0.527532481919951[/C][/ROW]
[ROW][C]35[/C][C]0.584227659846452[/C][C]0.831544680307096[/C][C]0.415772340153548[/C][/ROW]
[ROW][C]36[/C][C]0.503155061335456[/C][C]0.993689877329088[/C][C]0.496844938664544[/C][/ROW]
[ROW][C]37[/C][C]0.481835235066548[/C][C]0.963670470133096[/C][C]0.518164764933452[/C][/ROW]
[ROW][C]38[/C][C]0.637620692602485[/C][C]0.72475861479503[/C][C]0.362379307397515[/C][/ROW]
[ROW][C]39[/C][C]0.643107863314867[/C][C]0.713784273370266[/C][C]0.356892136685133[/C][/ROW]
[ROW][C]40[/C][C]0.550840307783422[/C][C]0.898319384433156[/C][C]0.449159692216578[/C][/ROW]
[ROW][C]41[/C][C]0.45423649265208[/C][C]0.90847298530416[/C][C]0.54576350734792[/C][/ROW]
[ROW][C]42[/C][C]0.47853608588757[/C][C]0.95707217177514[/C][C]0.52146391411243[/C][/ROW]
[ROW][C]43[/C][C]0.378423475767937[/C][C]0.756846951535873[/C][C]0.621576524232063[/C][/ROW]
[ROW][C]44[/C][C]0.287123754753799[/C][C]0.574247509507599[/C][C]0.7128762452462[/C][/ROW]
[ROW][C]45[/C][C]0.405400573274817[/C][C]0.810801146549635[/C][C]0.594599426725183[/C][/ROW]
[ROW][C]46[/C][C]0.445712783269265[/C][C]0.89142556653853[/C][C]0.554287216730735[/C][/ROW]
[ROW][C]47[/C][C]0.323004618765948[/C][C]0.646009237531896[/C][C]0.676995381234052[/C][/ROW]
[ROW][C]48[/C][C]0.218364660504086[/C][C]0.436729321008172[/C][C]0.781635339495914[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57778&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57778&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
90.533466117115880.9330677657682410.466533882884121
100.4312993872428890.8625987744857770.568700612757111
110.3624464184824820.7248928369649630.637553581517518
120.2846606541542330.5693213083084660.715339345845767
130.2561332413166170.5122664826332330.743866758683383
140.2522026618773890.5044053237547790.74779733812261
150.6804919267396140.6390161465207720.319508073260386
160.6087996051732750.782400789653450.391200394826725
170.5176354446558320.9647291106883360.482364555344168
180.5514993328544220.8970013342911550.448500667145578
190.4690076077022570.9380152154045140.530992392297743
200.4297271553678010.8594543107356020.570272844632199
210.439338267360170.878676534720340.56066173263983
220.3825402978842160.7650805957684310.617459702115784
230.4711456124772710.9422912249545430.528854387522729
240.4742451890696360.9484903781392730.525754810930364
250.4430419289161650.886083857832330.556958071083835
260.4210982409910040.8421964819820080.578901759008996
270.6481066961945570.7037866076108870.351893303805443
280.6109827327234350.778034534553130.389017267276565
290.5475819885072130.9048360229855750.452418011492787
300.6339568910346850.732086217930630.366043108965315
310.573010105716310.8539797885673810.426989894283691
320.4979969015025670.9959938030051340.502003098497433
330.5456998550495470.9086002899009050.454300144950453
340.4724675180800490.9449350361600970.527532481919951
350.5842276598464520.8315446803070960.415772340153548
360.5031550613354560.9936898773290880.496844938664544
370.4818352350665480.9636704701330960.518164764933452
380.6376206926024850.724758614795030.362379307397515
390.6431078633148670.7137842733702660.356892136685133
400.5508403077834220.8983193844331560.449159692216578
410.454236492652080.908472985304160.54576350734792
420.478536085887570.957072171775140.52146391411243
430.3784234757679370.7568469515358730.621576524232063
440.2871237547537990.5742475095075990.7128762452462
450.4054005732748170.8108011465496350.594599426725183
460.4457127832692650.891425566538530.554287216730735
470.3230046187659480.6460092375318960.676995381234052
480.2183646605040860.4367293210081720.781635339495914







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=57778&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=57778&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57778&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')
}