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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 12:02:44 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t1258743980v4vobiw3w7nnp4h.htm/, Retrieved Fri, 29 Mar 2024 12:33:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58426, Retrieved Fri, 29 Mar 2024 12:33:36 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact174
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] [JJ Workshop 7, Mu...] [2009-11-20 19:02:44] [e31f2fa83f4a5291b9a51009566cf69b] [Current]
-             [Multiple Regression] [Paper, Multiple R...] [2009-12-25 13:56:37] [96e597a9107bfe8c07649cce3d4f6fec]
-   PD          [Multiple Regression] [Paper, Multiple R...] [2009-12-26 14:04:00] [96e597a9107bfe8c07649cce3d4f6fec]
-   P             [Multiple Regression] [Paper, Mutliple R...] [2009-12-27 10:55:29] [96e597a9107bfe8c07649cce3d4f6fec]
-    D            [Multiple Regression] [Paper, Multiple R...] [2009-12-27 11:35:41] [96e597a9107bfe8c07649cce3d4f6fec]
-    D            [Multiple Regression] [Paper, Multiple R...] [2009-12-27 11:45:56] [96e597a9107bfe8c07649cce3d4f6fec]
-    D            [Multiple Regression] [Paper, Multiple R...] [2009-12-27 11:57:17] [96e597a9107bfe8c07649cce3d4f6fec]
-    D            [Multiple Regression] [Paper, Multiple R...] [2009-12-27 12:24:15] [96e597a9107bfe8c07649cce3d4f6fec]
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Dataseries X:
95,1	93,8
97	93,8
112,7	107,6
102,9	101
97,4	95,4
111,4	96,5
87,4	89,2
96,8	87,1
114,1	110,5
110,3	110,8
103,9	104,2
101,6	88,9
94,6	89,8
95,9	90
104,7	93,9
102,8	91,3
98,1	87,8
113,9	99,7
80,9	73,5
95,7	79,2
113,2	96,9
105,9	95,2
108,8	95,6
102,3	89,7
99	92,8
100,7	88
115,5	101,1
100,7	92,7
109,9	95,8
114,6	103,8
85,4	81,8
100,5	87,1
114,8	105,9
116,5	108,1
112,9	102,6
102	93,7
106	103,5
105,3	100,6
118,8	113,3
106,1	102,4
109,3	102,1
117,2	106,9
92,5	87,3
104,2	93,1
112,5	109,1
122,4	120,3
113,3	104,9
100	92,6
110,7	109,8
112,8	111,4
109,8	117,9
117,3	121,6
109,1	117,8
115,9	124,2
96	106,8
99,8	102,7
116,8	116,8
115,7	113,6
99,4	96,1
94,3	85




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=58426&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=58426&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58426&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
TIA[t] = + 39.3947409484312 + 0.663042805057097IAidM[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TIA[t] =  +  39.3947409484312 +  0.663042805057097IAidM[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58426&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TIA[t] =  +  39.3947409484312 +  0.663042805057097IAidM[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58426&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58426&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
TIA[t] = + 39.3947409484312 + 0.663042805057097IAidM[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)39.39474094843126.2252316.328200
IAidM0.6630428050570970.06214210.669800

\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) & 39.3947409484312 & 6.225231 & 6.3282 & 0 & 0 \tabularnewline
IAidM & 0.663042805057097 & 0.062142 & 10.6698 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58426&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]39.3947409484312[/C][C]6.225231[/C][C]6.3282[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]IAidM[/C][C]0.663042805057097[/C][C]0.062142[/C][C]10.6698[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58426&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58426&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)39.39474094843126.2252316.328200
IAidM0.6630428050570970.06214210.669800







Multiple Linear Regression - Regression Statistics
Multiple R0.813932329770039
R-squared0.662485837444884
Adjusted R-squared0.656666627745657
F-TEST (value)113.844640713495
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value2.66453525910038e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5.27333687938982
Sum Squared Residuals1612.86874692490

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.813932329770039 \tabularnewline
R-squared & 0.662485837444884 \tabularnewline
Adjusted R-squared & 0.656666627745657 \tabularnewline
F-TEST (value) & 113.844640713495 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 2.66453525910038e-15 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 5.27333687938982 \tabularnewline
Sum Squared Residuals & 1612.86874692490 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58426&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.813932329770039[/C][/ROW]
[ROW][C]R-squared[/C][C]0.662485837444884[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.656666627745657[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]113.844640713495[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]2.66453525910038e-15[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]5.27333687938982[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1612.86874692490[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58426&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58426&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 R0.813932329770039
R-squared0.662485837444884
Adjusted R-squared0.656666627745657
F-TEST (value)113.844640713495
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value2.66453525910038e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5.27333687938982
Sum Squared Residuals1612.86874692490







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
195.1101.588156062786-6.48815606278646
297101.588156062787-4.58815606278681
3112.7110.7381467725751.96185322742522
4102.9106.362064259198-3.46206425919794
597.4102.649024550878-5.2490245508782
6111.4103.3783716364418.021628363559
787.498.5381591595242-11.1381591595242
896.897.1457692689043-0.345769268904290
9114.1112.6609709072401.43902909275963
10110.3112.859883748757-2.55988374875750
11103.9108.483801235381-4.58380123538065
12101.698.3392463180073.26075368199292
1394.698.9359848425585-4.33598484255846
1495.999.0685934035699-3.16859340356987
15104.7101.6544603432933.04553965670745
16102.899.9305490501442.8694509498559
1798.197.60989923244430.490100767555738
18113.9105.5001086126248.39989138737629
1980.988.1283871201278-7.22838712012777
2095.791.90773110895323.79226889104678
21113.2103.6435887584649.55641124153616
22105.9102.5164159898673.38358401013322
23108.8102.7816331118906.01836688811038
24102.398.86968056205273.43031943794725
2599100.925113257730-1.92511325772974
26100.797.74250779345572.95749220654432
27115.5106.4283685397049.07163146029635
28100.7100.858808977224-0.158808977224035
29109.9102.9142416729016.98575832709897
30114.6108.2185841133586.38141588664218
3185.493.6316424021017-8.23164240210167
32100.597.14576926890433.35423073109571
33114.8109.6109740039785.18902599602227
34116.5111.0696681751035.43033182489667
35112.9107.4229327472895.47706725271071
36102101.5218517822810.478148217718865
37106108.019671271841-2.01967127184069
38105.3106.096847137175-0.796847137175103
39118.8114.5174907614004.28250923859976
40106.1107.290324186278-1.19032418627789
41109.3107.0914113447612.20858865523925
42117.2110.2740168090356.92598319096518
4392.597.2783778299157-4.77837782991571
44104.2101.1240260992473.07597390075313
45112.5111.7327109801600.767289019839573
46122.4119.15879039683.24120960320009
47113.3108.9479311989214.35206880107937
48100100.792504696718-0.792504696718322
49110.7112.196840943700-1.49684094370039
50112.8113.257709431792-0.45770943179176
51109.8117.567487664663-7.76748766466289
52117.3120.020746043374-2.72074604337414
53109.1117.501183384157-8.40118338415718
54115.9121.744657336523-5.8446573365226
5596110.207712528529-14.2077125285291
5699.8107.489237027795-7.68923702779501
57116.8116.838140579100-0.0381405791000808
58115.7114.7164036029170.983596397082638
5999.4103.113154514418-3.71315451441816
6094.395.7533793782844-1.45337937828439

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 95.1 & 101.588156062786 & -6.48815606278646 \tabularnewline
2 & 97 & 101.588156062787 & -4.58815606278681 \tabularnewline
3 & 112.7 & 110.738146772575 & 1.96185322742522 \tabularnewline
4 & 102.9 & 106.362064259198 & -3.46206425919794 \tabularnewline
5 & 97.4 & 102.649024550878 & -5.2490245508782 \tabularnewline
6 & 111.4 & 103.378371636441 & 8.021628363559 \tabularnewline
7 & 87.4 & 98.5381591595242 & -11.1381591595242 \tabularnewline
8 & 96.8 & 97.1457692689043 & -0.345769268904290 \tabularnewline
9 & 114.1 & 112.660970907240 & 1.43902909275963 \tabularnewline
10 & 110.3 & 112.859883748757 & -2.55988374875750 \tabularnewline
11 & 103.9 & 108.483801235381 & -4.58380123538065 \tabularnewline
12 & 101.6 & 98.339246318007 & 3.26075368199292 \tabularnewline
13 & 94.6 & 98.9359848425585 & -4.33598484255846 \tabularnewline
14 & 95.9 & 99.0685934035699 & -3.16859340356987 \tabularnewline
15 & 104.7 & 101.654460343293 & 3.04553965670745 \tabularnewline
16 & 102.8 & 99.930549050144 & 2.8694509498559 \tabularnewline
17 & 98.1 & 97.6098992324443 & 0.490100767555738 \tabularnewline
18 & 113.9 & 105.500108612624 & 8.39989138737629 \tabularnewline
19 & 80.9 & 88.1283871201278 & -7.22838712012777 \tabularnewline
20 & 95.7 & 91.9077311089532 & 3.79226889104678 \tabularnewline
21 & 113.2 & 103.643588758464 & 9.55641124153616 \tabularnewline
22 & 105.9 & 102.516415989867 & 3.38358401013322 \tabularnewline
23 & 108.8 & 102.781633111890 & 6.01836688811038 \tabularnewline
24 & 102.3 & 98.8696805620527 & 3.43031943794725 \tabularnewline
25 & 99 & 100.925113257730 & -1.92511325772974 \tabularnewline
26 & 100.7 & 97.7425077934557 & 2.95749220654432 \tabularnewline
27 & 115.5 & 106.428368539704 & 9.07163146029635 \tabularnewline
28 & 100.7 & 100.858808977224 & -0.158808977224035 \tabularnewline
29 & 109.9 & 102.914241672901 & 6.98575832709897 \tabularnewline
30 & 114.6 & 108.218584113358 & 6.38141588664218 \tabularnewline
31 & 85.4 & 93.6316424021017 & -8.23164240210167 \tabularnewline
32 & 100.5 & 97.1457692689043 & 3.35423073109571 \tabularnewline
33 & 114.8 & 109.610974003978 & 5.18902599602227 \tabularnewline
34 & 116.5 & 111.069668175103 & 5.43033182489667 \tabularnewline
35 & 112.9 & 107.422932747289 & 5.47706725271071 \tabularnewline
36 & 102 & 101.521851782281 & 0.478148217718865 \tabularnewline
37 & 106 & 108.019671271841 & -2.01967127184069 \tabularnewline
38 & 105.3 & 106.096847137175 & -0.796847137175103 \tabularnewline
39 & 118.8 & 114.517490761400 & 4.28250923859976 \tabularnewline
40 & 106.1 & 107.290324186278 & -1.19032418627789 \tabularnewline
41 & 109.3 & 107.091411344761 & 2.20858865523925 \tabularnewline
42 & 117.2 & 110.274016809035 & 6.92598319096518 \tabularnewline
43 & 92.5 & 97.2783778299157 & -4.77837782991571 \tabularnewline
44 & 104.2 & 101.124026099247 & 3.07597390075313 \tabularnewline
45 & 112.5 & 111.732710980160 & 0.767289019839573 \tabularnewline
46 & 122.4 & 119.1587903968 & 3.24120960320009 \tabularnewline
47 & 113.3 & 108.947931198921 & 4.35206880107937 \tabularnewline
48 & 100 & 100.792504696718 & -0.792504696718322 \tabularnewline
49 & 110.7 & 112.196840943700 & -1.49684094370039 \tabularnewline
50 & 112.8 & 113.257709431792 & -0.45770943179176 \tabularnewline
51 & 109.8 & 117.567487664663 & -7.76748766466289 \tabularnewline
52 & 117.3 & 120.020746043374 & -2.72074604337414 \tabularnewline
53 & 109.1 & 117.501183384157 & -8.40118338415718 \tabularnewline
54 & 115.9 & 121.744657336523 & -5.8446573365226 \tabularnewline
55 & 96 & 110.207712528529 & -14.2077125285291 \tabularnewline
56 & 99.8 & 107.489237027795 & -7.68923702779501 \tabularnewline
57 & 116.8 & 116.838140579100 & -0.0381405791000808 \tabularnewline
58 & 115.7 & 114.716403602917 & 0.983596397082638 \tabularnewline
59 & 99.4 & 103.113154514418 & -3.71315451441816 \tabularnewline
60 & 94.3 & 95.7533793782844 & -1.45337937828439 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58426&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]101.588156062786[/C][C]-6.48815606278646[/C][/ROW]
[ROW][C]2[/C][C]97[/C][C]101.588156062787[/C][C]-4.58815606278681[/C][/ROW]
[ROW][C]3[/C][C]112.7[/C][C]110.738146772575[/C][C]1.96185322742522[/C][/ROW]
[ROW][C]4[/C][C]102.9[/C][C]106.362064259198[/C][C]-3.46206425919794[/C][/ROW]
[ROW][C]5[/C][C]97.4[/C][C]102.649024550878[/C][C]-5.2490245508782[/C][/ROW]
[ROW][C]6[/C][C]111.4[/C][C]103.378371636441[/C][C]8.021628363559[/C][/ROW]
[ROW][C]7[/C][C]87.4[/C][C]98.5381591595242[/C][C]-11.1381591595242[/C][/ROW]
[ROW][C]8[/C][C]96.8[/C][C]97.1457692689043[/C][C]-0.345769268904290[/C][/ROW]
[ROW][C]9[/C][C]114.1[/C][C]112.660970907240[/C][C]1.43902909275963[/C][/ROW]
[ROW][C]10[/C][C]110.3[/C][C]112.859883748757[/C][C]-2.55988374875750[/C][/ROW]
[ROW][C]11[/C][C]103.9[/C][C]108.483801235381[/C][C]-4.58380123538065[/C][/ROW]
[ROW][C]12[/C][C]101.6[/C][C]98.339246318007[/C][C]3.26075368199292[/C][/ROW]
[ROW][C]13[/C][C]94.6[/C][C]98.9359848425585[/C][C]-4.33598484255846[/C][/ROW]
[ROW][C]14[/C][C]95.9[/C][C]99.0685934035699[/C][C]-3.16859340356987[/C][/ROW]
[ROW][C]15[/C][C]104.7[/C][C]101.654460343293[/C][C]3.04553965670745[/C][/ROW]
[ROW][C]16[/C][C]102.8[/C][C]99.930549050144[/C][C]2.8694509498559[/C][/ROW]
[ROW][C]17[/C][C]98.1[/C][C]97.6098992324443[/C][C]0.490100767555738[/C][/ROW]
[ROW][C]18[/C][C]113.9[/C][C]105.500108612624[/C][C]8.39989138737629[/C][/ROW]
[ROW][C]19[/C][C]80.9[/C][C]88.1283871201278[/C][C]-7.22838712012777[/C][/ROW]
[ROW][C]20[/C][C]95.7[/C][C]91.9077311089532[/C][C]3.79226889104678[/C][/ROW]
[ROW][C]21[/C][C]113.2[/C][C]103.643588758464[/C][C]9.55641124153616[/C][/ROW]
[ROW][C]22[/C][C]105.9[/C][C]102.516415989867[/C][C]3.38358401013322[/C][/ROW]
[ROW][C]23[/C][C]108.8[/C][C]102.781633111890[/C][C]6.01836688811038[/C][/ROW]
[ROW][C]24[/C][C]102.3[/C][C]98.8696805620527[/C][C]3.43031943794725[/C][/ROW]
[ROW][C]25[/C][C]99[/C][C]100.925113257730[/C][C]-1.92511325772974[/C][/ROW]
[ROW][C]26[/C][C]100.7[/C][C]97.7425077934557[/C][C]2.95749220654432[/C][/ROW]
[ROW][C]27[/C][C]115.5[/C][C]106.428368539704[/C][C]9.07163146029635[/C][/ROW]
[ROW][C]28[/C][C]100.7[/C][C]100.858808977224[/C][C]-0.158808977224035[/C][/ROW]
[ROW][C]29[/C][C]109.9[/C][C]102.914241672901[/C][C]6.98575832709897[/C][/ROW]
[ROW][C]30[/C][C]114.6[/C][C]108.218584113358[/C][C]6.38141588664218[/C][/ROW]
[ROW][C]31[/C][C]85.4[/C][C]93.6316424021017[/C][C]-8.23164240210167[/C][/ROW]
[ROW][C]32[/C][C]100.5[/C][C]97.1457692689043[/C][C]3.35423073109571[/C][/ROW]
[ROW][C]33[/C][C]114.8[/C][C]109.610974003978[/C][C]5.18902599602227[/C][/ROW]
[ROW][C]34[/C][C]116.5[/C][C]111.069668175103[/C][C]5.43033182489667[/C][/ROW]
[ROW][C]35[/C][C]112.9[/C][C]107.422932747289[/C][C]5.47706725271071[/C][/ROW]
[ROW][C]36[/C][C]102[/C][C]101.521851782281[/C][C]0.478148217718865[/C][/ROW]
[ROW][C]37[/C][C]106[/C][C]108.019671271841[/C][C]-2.01967127184069[/C][/ROW]
[ROW][C]38[/C][C]105.3[/C][C]106.096847137175[/C][C]-0.796847137175103[/C][/ROW]
[ROW][C]39[/C][C]118.8[/C][C]114.517490761400[/C][C]4.28250923859976[/C][/ROW]
[ROW][C]40[/C][C]106.1[/C][C]107.290324186278[/C][C]-1.19032418627789[/C][/ROW]
[ROW][C]41[/C][C]109.3[/C][C]107.091411344761[/C][C]2.20858865523925[/C][/ROW]
[ROW][C]42[/C][C]117.2[/C][C]110.274016809035[/C][C]6.92598319096518[/C][/ROW]
[ROW][C]43[/C][C]92.5[/C][C]97.2783778299157[/C][C]-4.77837782991571[/C][/ROW]
[ROW][C]44[/C][C]104.2[/C][C]101.124026099247[/C][C]3.07597390075313[/C][/ROW]
[ROW][C]45[/C][C]112.5[/C][C]111.732710980160[/C][C]0.767289019839573[/C][/ROW]
[ROW][C]46[/C][C]122.4[/C][C]119.1587903968[/C][C]3.24120960320009[/C][/ROW]
[ROW][C]47[/C][C]113.3[/C][C]108.947931198921[/C][C]4.35206880107937[/C][/ROW]
[ROW][C]48[/C][C]100[/C][C]100.792504696718[/C][C]-0.792504696718322[/C][/ROW]
[ROW][C]49[/C][C]110.7[/C][C]112.196840943700[/C][C]-1.49684094370039[/C][/ROW]
[ROW][C]50[/C][C]112.8[/C][C]113.257709431792[/C][C]-0.45770943179176[/C][/ROW]
[ROW][C]51[/C][C]109.8[/C][C]117.567487664663[/C][C]-7.76748766466289[/C][/ROW]
[ROW][C]52[/C][C]117.3[/C][C]120.020746043374[/C][C]-2.72074604337414[/C][/ROW]
[ROW][C]53[/C][C]109.1[/C][C]117.501183384157[/C][C]-8.40118338415718[/C][/ROW]
[ROW][C]54[/C][C]115.9[/C][C]121.744657336523[/C][C]-5.8446573365226[/C][/ROW]
[ROW][C]55[/C][C]96[/C][C]110.207712528529[/C][C]-14.2077125285291[/C][/ROW]
[ROW][C]56[/C][C]99.8[/C][C]107.489237027795[/C][C]-7.68923702779501[/C][/ROW]
[ROW][C]57[/C][C]116.8[/C][C]116.838140579100[/C][C]-0.0381405791000808[/C][/ROW]
[ROW][C]58[/C][C]115.7[/C][C]114.716403602917[/C][C]0.983596397082638[/C][/ROW]
[ROW][C]59[/C][C]99.4[/C][C]103.113154514418[/C][C]-3.71315451441816[/C][/ROW]
[ROW][C]60[/C][C]94.3[/C][C]95.7533793782844[/C][C]-1.45337937828439[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58426&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58426&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
195.1101.588156062786-6.48815606278646
297101.588156062787-4.58815606278681
3112.7110.7381467725751.96185322742522
4102.9106.362064259198-3.46206425919794
597.4102.649024550878-5.2490245508782
6111.4103.3783716364418.021628363559
787.498.5381591595242-11.1381591595242
896.897.1457692689043-0.345769268904290
9114.1112.6609709072401.43902909275963
10110.3112.859883748757-2.55988374875750
11103.9108.483801235381-4.58380123538065
12101.698.3392463180073.26075368199292
1394.698.9359848425585-4.33598484255846
1495.999.0685934035699-3.16859340356987
15104.7101.6544603432933.04553965670745
16102.899.9305490501442.8694509498559
1798.197.60989923244430.490100767555738
18113.9105.5001086126248.39989138737629
1980.988.1283871201278-7.22838712012777
2095.791.90773110895323.79226889104678
21113.2103.6435887584649.55641124153616
22105.9102.5164159898673.38358401013322
23108.8102.7816331118906.01836688811038
24102.398.86968056205273.43031943794725
2599100.925113257730-1.92511325772974
26100.797.74250779345572.95749220654432
27115.5106.4283685397049.07163146029635
28100.7100.858808977224-0.158808977224035
29109.9102.9142416729016.98575832709897
30114.6108.2185841133586.38141588664218
3185.493.6316424021017-8.23164240210167
32100.597.14576926890433.35423073109571
33114.8109.6109740039785.18902599602227
34116.5111.0696681751035.43033182489667
35112.9107.4229327472895.47706725271071
36102101.5218517822810.478148217718865
37106108.019671271841-2.01967127184069
38105.3106.096847137175-0.796847137175103
39118.8114.5174907614004.28250923859976
40106.1107.290324186278-1.19032418627789
41109.3107.0914113447612.20858865523925
42117.2110.2740168090356.92598319096518
4392.597.2783778299157-4.77837782991571
44104.2101.1240260992473.07597390075313
45112.5111.7327109801600.767289019839573
46122.4119.15879039683.24120960320009
47113.3108.9479311989214.35206880107937
48100100.792504696718-0.792504696718322
49110.7112.196840943700-1.49684094370039
50112.8113.257709431792-0.45770943179176
51109.8117.567487664663-7.76748766466289
52117.3120.020746043374-2.72074604337414
53109.1117.501183384157-8.40118338415718
54115.9121.744657336523-5.8446573365226
5596110.207712528529-14.2077125285291
5699.8107.489237027795-7.68923702779501
57116.8116.838140579100-0.0381405791000808
58115.7114.7164036029170.983596397082638
5999.4103.113154514418-3.71315451441816
6094.395.7533793782844-1.45337937828439







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.01510666191317610.03021332382635210.984893338086824
60.6774759469974490.6450481060051020.322524053002551
70.6808024575420270.6383950849159470.319197542457973
80.7161624313213690.5676751373572630.283837568678631
90.6101672173858870.7796655652282260.389832782614113
100.5538259067782630.8923481864434730.446174093221737
110.4985737624285180.9971475248570370.501426237571482
120.5516304351171720.8967391297656560.448369564882828
130.4738493112486270.9476986224972530.526150688751373
140.3897503467839430.7795006935678850.610249653216057
150.3843462342147760.7686924684295520.615653765785224
160.36668617156930.73337234313860.6333138284307
170.301341069177980.602682138355960.69865893082202
180.4590532526973990.9181065053947970.540946747302601
190.4710079487555720.9420158975111430.528992051244428
200.4849245579288960.969849115857790.515075442071104
210.661042018893860.6779159622122810.338957981106141
220.6150465105506710.7699069788986580.384953489449329
230.6284127625079580.7431744749840850.371587237492042
240.5847265032186050.830546993562790.415273496781395
250.5184973507677870.9630052984644260.481502649232213
260.4660952501678150.9321905003356310.533904749832185
270.5843590917625460.8312818164749080.415640908237454
280.5084724290429350.983055141914130.491527570957065
290.5548557083033030.8902885833933940.445144291696697
300.5728160124291670.8543679751416660.427183987570833
310.6549358176348540.6901283647302930.345064182365146
320.6147140077388450.770571984522310.385285992261155
330.603745043713310.7925099125733790.396254956286689
340.6061050403825860.7877899192348280.393894959617414
350.6195917398938660.7608165202122680.380408260106134
360.5475588992866470.9048822014267050.452441100713353
370.49519059074980.99038118149960.5048094092502
380.4253624525106210.8507249050212420.574637547489379
390.4219850153714180.8439700307428360.578014984628582
400.3565235553346570.7130471106693140.643476444665343
410.3113209993131730.6226419986263460.688679000686827
420.4287900779169290.8575801558338580.571209922083071
430.3881052952082320.7762105904164630.611894704791768
440.3711225643626020.7422451287252030.628877435637398
450.3289596643134030.6579193286268060.671040335686597
460.38002439021910.76004878043820.6199756097809
470.4921951366322610.9843902732645230.507804863367739
480.4229265021111850.845853004222370.577073497888815
490.3743282944624460.7486565889248920.625671705537554
500.3546547850192430.7093095700384870.645345214980757
510.3572370184217450.714474036843490.642762981578255
520.2883432656653150.576686531330630.711656734334685
530.2653043992675840.5306087985351670.734695600732416
540.1862568293926940.3725136587853870.813743170607306
550.6958173197813470.6083653604373060.304182680218653

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.0151066619131761 & 0.0302133238263521 & 0.984893338086824 \tabularnewline
6 & 0.677475946997449 & 0.645048106005102 & 0.322524053002551 \tabularnewline
7 & 0.680802457542027 & 0.638395084915947 & 0.319197542457973 \tabularnewline
8 & 0.716162431321369 & 0.567675137357263 & 0.283837568678631 \tabularnewline
9 & 0.610167217385887 & 0.779665565228226 & 0.389832782614113 \tabularnewline
10 & 0.553825906778263 & 0.892348186443473 & 0.446174093221737 \tabularnewline
11 & 0.498573762428518 & 0.997147524857037 & 0.501426237571482 \tabularnewline
12 & 0.551630435117172 & 0.896739129765656 & 0.448369564882828 \tabularnewline
13 & 0.473849311248627 & 0.947698622497253 & 0.526150688751373 \tabularnewline
14 & 0.389750346783943 & 0.779500693567885 & 0.610249653216057 \tabularnewline
15 & 0.384346234214776 & 0.768692468429552 & 0.615653765785224 \tabularnewline
16 & 0.3666861715693 & 0.7333723431386 & 0.6333138284307 \tabularnewline
17 & 0.30134106917798 & 0.60268213835596 & 0.69865893082202 \tabularnewline
18 & 0.459053252697399 & 0.918106505394797 & 0.540946747302601 \tabularnewline
19 & 0.471007948755572 & 0.942015897511143 & 0.528992051244428 \tabularnewline
20 & 0.484924557928896 & 0.96984911585779 & 0.515075442071104 \tabularnewline
21 & 0.66104201889386 & 0.677915962212281 & 0.338957981106141 \tabularnewline
22 & 0.615046510550671 & 0.769906978898658 & 0.384953489449329 \tabularnewline
23 & 0.628412762507958 & 0.743174474984085 & 0.371587237492042 \tabularnewline
24 & 0.584726503218605 & 0.83054699356279 & 0.415273496781395 \tabularnewline
25 & 0.518497350767787 & 0.963005298464426 & 0.481502649232213 \tabularnewline
26 & 0.466095250167815 & 0.932190500335631 & 0.533904749832185 \tabularnewline
27 & 0.584359091762546 & 0.831281816474908 & 0.415640908237454 \tabularnewline
28 & 0.508472429042935 & 0.98305514191413 & 0.491527570957065 \tabularnewline
29 & 0.554855708303303 & 0.890288583393394 & 0.445144291696697 \tabularnewline
30 & 0.572816012429167 & 0.854367975141666 & 0.427183987570833 \tabularnewline
31 & 0.654935817634854 & 0.690128364730293 & 0.345064182365146 \tabularnewline
32 & 0.614714007738845 & 0.77057198452231 & 0.385285992261155 \tabularnewline
33 & 0.60374504371331 & 0.792509912573379 & 0.396254956286689 \tabularnewline
34 & 0.606105040382586 & 0.787789919234828 & 0.393894959617414 \tabularnewline
35 & 0.619591739893866 & 0.760816520212268 & 0.380408260106134 \tabularnewline
36 & 0.547558899286647 & 0.904882201426705 & 0.452441100713353 \tabularnewline
37 & 0.4951905907498 & 0.9903811814996 & 0.5048094092502 \tabularnewline
38 & 0.425362452510621 & 0.850724905021242 & 0.574637547489379 \tabularnewline
39 & 0.421985015371418 & 0.843970030742836 & 0.578014984628582 \tabularnewline
40 & 0.356523555334657 & 0.713047110669314 & 0.643476444665343 \tabularnewline
41 & 0.311320999313173 & 0.622641998626346 & 0.688679000686827 \tabularnewline
42 & 0.428790077916929 & 0.857580155833858 & 0.571209922083071 \tabularnewline
43 & 0.388105295208232 & 0.776210590416463 & 0.611894704791768 \tabularnewline
44 & 0.371122564362602 & 0.742245128725203 & 0.628877435637398 \tabularnewline
45 & 0.328959664313403 & 0.657919328626806 & 0.671040335686597 \tabularnewline
46 & 0.3800243902191 & 0.7600487804382 & 0.6199756097809 \tabularnewline
47 & 0.492195136632261 & 0.984390273264523 & 0.507804863367739 \tabularnewline
48 & 0.422926502111185 & 0.84585300422237 & 0.577073497888815 \tabularnewline
49 & 0.374328294462446 & 0.748656588924892 & 0.625671705537554 \tabularnewline
50 & 0.354654785019243 & 0.709309570038487 & 0.645345214980757 \tabularnewline
51 & 0.357237018421745 & 0.71447403684349 & 0.642762981578255 \tabularnewline
52 & 0.288343265665315 & 0.57668653133063 & 0.711656734334685 \tabularnewline
53 & 0.265304399267584 & 0.530608798535167 & 0.734695600732416 \tabularnewline
54 & 0.186256829392694 & 0.372513658785387 & 0.813743170607306 \tabularnewline
55 & 0.695817319781347 & 0.608365360437306 & 0.304182680218653 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58426&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]0.0151066619131761[/C][C]0.0302133238263521[/C][C]0.984893338086824[/C][/ROW]
[ROW][C]6[/C][C]0.677475946997449[/C][C]0.645048106005102[/C][C]0.322524053002551[/C][/ROW]
[ROW][C]7[/C][C]0.680802457542027[/C][C]0.638395084915947[/C][C]0.319197542457973[/C][/ROW]
[ROW][C]8[/C][C]0.716162431321369[/C][C]0.567675137357263[/C][C]0.283837568678631[/C][/ROW]
[ROW][C]9[/C][C]0.610167217385887[/C][C]0.779665565228226[/C][C]0.389832782614113[/C][/ROW]
[ROW][C]10[/C][C]0.553825906778263[/C][C]0.892348186443473[/C][C]0.446174093221737[/C][/ROW]
[ROW][C]11[/C][C]0.498573762428518[/C][C]0.997147524857037[/C][C]0.501426237571482[/C][/ROW]
[ROW][C]12[/C][C]0.551630435117172[/C][C]0.896739129765656[/C][C]0.448369564882828[/C][/ROW]
[ROW][C]13[/C][C]0.473849311248627[/C][C]0.947698622497253[/C][C]0.526150688751373[/C][/ROW]
[ROW][C]14[/C][C]0.389750346783943[/C][C]0.779500693567885[/C][C]0.610249653216057[/C][/ROW]
[ROW][C]15[/C][C]0.384346234214776[/C][C]0.768692468429552[/C][C]0.615653765785224[/C][/ROW]
[ROW][C]16[/C][C]0.3666861715693[/C][C]0.7333723431386[/C][C]0.6333138284307[/C][/ROW]
[ROW][C]17[/C][C]0.30134106917798[/C][C]0.60268213835596[/C][C]0.69865893082202[/C][/ROW]
[ROW][C]18[/C][C]0.459053252697399[/C][C]0.918106505394797[/C][C]0.540946747302601[/C][/ROW]
[ROW][C]19[/C][C]0.471007948755572[/C][C]0.942015897511143[/C][C]0.528992051244428[/C][/ROW]
[ROW][C]20[/C][C]0.484924557928896[/C][C]0.96984911585779[/C][C]0.515075442071104[/C][/ROW]
[ROW][C]21[/C][C]0.66104201889386[/C][C]0.677915962212281[/C][C]0.338957981106141[/C][/ROW]
[ROW][C]22[/C][C]0.615046510550671[/C][C]0.769906978898658[/C][C]0.384953489449329[/C][/ROW]
[ROW][C]23[/C][C]0.628412762507958[/C][C]0.743174474984085[/C][C]0.371587237492042[/C][/ROW]
[ROW][C]24[/C][C]0.584726503218605[/C][C]0.83054699356279[/C][C]0.415273496781395[/C][/ROW]
[ROW][C]25[/C][C]0.518497350767787[/C][C]0.963005298464426[/C][C]0.481502649232213[/C][/ROW]
[ROW][C]26[/C][C]0.466095250167815[/C][C]0.932190500335631[/C][C]0.533904749832185[/C][/ROW]
[ROW][C]27[/C][C]0.584359091762546[/C][C]0.831281816474908[/C][C]0.415640908237454[/C][/ROW]
[ROW][C]28[/C][C]0.508472429042935[/C][C]0.98305514191413[/C][C]0.491527570957065[/C][/ROW]
[ROW][C]29[/C][C]0.554855708303303[/C][C]0.890288583393394[/C][C]0.445144291696697[/C][/ROW]
[ROW][C]30[/C][C]0.572816012429167[/C][C]0.854367975141666[/C][C]0.427183987570833[/C][/ROW]
[ROW][C]31[/C][C]0.654935817634854[/C][C]0.690128364730293[/C][C]0.345064182365146[/C][/ROW]
[ROW][C]32[/C][C]0.614714007738845[/C][C]0.77057198452231[/C][C]0.385285992261155[/C][/ROW]
[ROW][C]33[/C][C]0.60374504371331[/C][C]0.792509912573379[/C][C]0.396254956286689[/C][/ROW]
[ROW][C]34[/C][C]0.606105040382586[/C][C]0.787789919234828[/C][C]0.393894959617414[/C][/ROW]
[ROW][C]35[/C][C]0.619591739893866[/C][C]0.760816520212268[/C][C]0.380408260106134[/C][/ROW]
[ROW][C]36[/C][C]0.547558899286647[/C][C]0.904882201426705[/C][C]0.452441100713353[/C][/ROW]
[ROW][C]37[/C][C]0.4951905907498[/C][C]0.9903811814996[/C][C]0.5048094092502[/C][/ROW]
[ROW][C]38[/C][C]0.425362452510621[/C][C]0.850724905021242[/C][C]0.574637547489379[/C][/ROW]
[ROW][C]39[/C][C]0.421985015371418[/C][C]0.843970030742836[/C][C]0.578014984628582[/C][/ROW]
[ROW][C]40[/C][C]0.356523555334657[/C][C]0.713047110669314[/C][C]0.643476444665343[/C][/ROW]
[ROW][C]41[/C][C]0.311320999313173[/C][C]0.622641998626346[/C][C]0.688679000686827[/C][/ROW]
[ROW][C]42[/C][C]0.428790077916929[/C][C]0.857580155833858[/C][C]0.571209922083071[/C][/ROW]
[ROW][C]43[/C][C]0.388105295208232[/C][C]0.776210590416463[/C][C]0.611894704791768[/C][/ROW]
[ROW][C]44[/C][C]0.371122564362602[/C][C]0.742245128725203[/C][C]0.628877435637398[/C][/ROW]
[ROW][C]45[/C][C]0.328959664313403[/C][C]0.657919328626806[/C][C]0.671040335686597[/C][/ROW]
[ROW][C]46[/C][C]0.3800243902191[/C][C]0.7600487804382[/C][C]0.6199756097809[/C][/ROW]
[ROW][C]47[/C][C]0.492195136632261[/C][C]0.984390273264523[/C][C]0.507804863367739[/C][/ROW]
[ROW][C]48[/C][C]0.422926502111185[/C][C]0.84585300422237[/C][C]0.577073497888815[/C][/ROW]
[ROW][C]49[/C][C]0.374328294462446[/C][C]0.748656588924892[/C][C]0.625671705537554[/C][/ROW]
[ROW][C]50[/C][C]0.354654785019243[/C][C]0.709309570038487[/C][C]0.645345214980757[/C][/ROW]
[ROW][C]51[/C][C]0.357237018421745[/C][C]0.71447403684349[/C][C]0.642762981578255[/C][/ROW]
[ROW][C]52[/C][C]0.288343265665315[/C][C]0.57668653133063[/C][C]0.711656734334685[/C][/ROW]
[ROW][C]53[/C][C]0.265304399267584[/C][C]0.530608798535167[/C][C]0.734695600732416[/C][/ROW]
[ROW][C]54[/C][C]0.186256829392694[/C][C]0.372513658785387[/C][C]0.813743170607306[/C][/ROW]
[ROW][C]55[/C][C]0.695817319781347[/C][C]0.608365360437306[/C][C]0.304182680218653[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58426&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58426&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
50.01510666191317610.03021332382635210.984893338086824
60.6774759469974490.6450481060051020.322524053002551
70.6808024575420270.6383950849159470.319197542457973
80.7161624313213690.5676751373572630.283837568678631
90.6101672173858870.7796655652282260.389832782614113
100.5538259067782630.8923481864434730.446174093221737
110.4985737624285180.9971475248570370.501426237571482
120.5516304351171720.8967391297656560.448369564882828
130.4738493112486270.9476986224972530.526150688751373
140.3897503467839430.7795006935678850.610249653216057
150.3843462342147760.7686924684295520.615653765785224
160.36668617156930.73337234313860.6333138284307
170.301341069177980.602682138355960.69865893082202
180.4590532526973990.9181065053947970.540946747302601
190.4710079487555720.9420158975111430.528992051244428
200.4849245579288960.969849115857790.515075442071104
210.661042018893860.6779159622122810.338957981106141
220.6150465105506710.7699069788986580.384953489449329
230.6284127625079580.7431744749840850.371587237492042
240.5847265032186050.830546993562790.415273496781395
250.5184973507677870.9630052984644260.481502649232213
260.4660952501678150.9321905003356310.533904749832185
270.5843590917625460.8312818164749080.415640908237454
280.5084724290429350.983055141914130.491527570957065
290.5548557083033030.8902885833933940.445144291696697
300.5728160124291670.8543679751416660.427183987570833
310.6549358176348540.6901283647302930.345064182365146
320.6147140077388450.770571984522310.385285992261155
330.603745043713310.7925099125733790.396254956286689
340.6061050403825860.7877899192348280.393894959617414
350.6195917398938660.7608165202122680.380408260106134
360.5475588992866470.9048822014267050.452441100713353
370.49519059074980.99038118149960.5048094092502
380.4253624525106210.8507249050212420.574637547489379
390.4219850153714180.8439700307428360.578014984628582
400.3565235553346570.7130471106693140.643476444665343
410.3113209993131730.6226419986263460.688679000686827
420.4287900779169290.8575801558338580.571209922083071
430.3881052952082320.7762105904164630.611894704791768
440.3711225643626020.7422451287252030.628877435637398
450.3289596643134030.6579193286268060.671040335686597
460.38002439021910.76004878043820.6199756097809
470.4921951366322610.9843902732645230.507804863367739
480.4229265021111850.845853004222370.577073497888815
490.3743282944624460.7486565889248920.625671705537554
500.3546547850192430.7093095700384870.645345214980757
510.3572370184217450.714474036843490.642762981578255
520.2883432656653150.576686531330630.711656734334685
530.2653043992675840.5306087985351670.734695600732416
540.1862568293926940.3725136587853870.813743170607306
550.6958173197813470.6083653604373060.304182680218653







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.0196078431372549OK
10% type I error level10.0196078431372549OK

\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 & 1 & 0.0196078431372549 & OK \tabularnewline
10% type I error level & 1 & 0.0196078431372549 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58426&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]1[/C][C]0.0196078431372549[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]1[/C][C]0.0196078431372549[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58426&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58426&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 level10.0196078431372549OK
10% type I error level10.0196078431372549OK



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')
}