## Free Statistics

of Irreproducible Research!

Author's title
Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 26 Nov 2011 13:25:06 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/26/t1322331927elygv8m38fnx55q.htm/, Retrieved Mon, 30 Jan 2023 00:48:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147438, Retrieved Mon, 30 Jan 2023 00:48:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact99
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 14:06:21] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [Multivariate regr...] [2009-11-19 09:22:31] [21324e9cdf3569788a3d630236984d87]
-    D      [Multiple Regression] [] [2010-12-07 12:41:22] [f47feae0308dca73181bb669fbad1c56]
- R             [Multiple Regression] [] [2011-11-26 18:25:06] [d41d8cd98f00b204e9800998ecf8427e] [Current]
- R P             [Multiple Regression] [] [2011-11-27 16:54:17] [3931071255a6f7f4a767409781cc5f7d]
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Dataseries X:
112.3	0
117.3	0
111.1	1
102.2	1
104.3	1
122.9	1
107.6	1
121.3	1
131.5	1
89	1
104.4	1
128.9	1
135.9	1
133.3	1
121.3	1
120.5	0
120.4	0
137.9	0
126.1	0
133.2	0
151.1	0
105	0
119	0
140.4	0
156.6	0
137.1	0
122.7	0
125.8	0
139.3	0
134.9	0
149.2	0
132.3	0
149	0
117.2	0
119.6	0
152	0
149.4	0
127.3	0
114.1	0
102.1	0
107.7	0
104.4	0
102.1	0
96	1
109.3	0
90	1
83.9	1
112	1
114.3	1
103.6	1
91.7	1
80.8	1
87.2	1
109.2	1
102.7	1
95.1	1
117.5	1
85.1	1
92.1	1
113.5	1


 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 5 seconds R Server 'George Udny Yule' @ yule.wessa.net

\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 & 5 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147438&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147438&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147438&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 Input view raw input (R code) Raw Output view raw output of R engine Computing time 5 seconds R Server 'George Udny Yule' @ yule.wessa.net

 Multiple Linear Regression - Estimated Regression Equation Promet[t] = + 152.062865531415 -18.0165590135056Dummy[t] -2.89726267371302M1[t] -12.5469035036211M2[t] -20.153232530828M3[t] -29.3261851634371M4[t] -23.4958259933451M5[t] -13.0854668232531M6[t] -17.0751076531611M7[t] -15.101436680368M8[t] -2.27438931297713M9[t] -32.760718340184M10[t] -25.890359170092M11[t] -0.330359170091995t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Promet[t] =  +  152.062865531415 -18.0165590135056Dummy[t] -2.89726267371302M1[t] -12.5469035036211M2[t] -20.153232530828M3[t] -29.3261851634371M4[t] -23.4958259933451M5[t] -13.0854668232531M6[t] -17.0751076531611M7[t] -15.101436680368M8[t] -2.27438931297713M9[t] -32.760718340184M10[t] -25.890359170092M11[t] -0.330359170091995t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147438&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Promet[t] =  +  152.062865531415 -18.0165590135056Dummy[t] -2.89726267371302M1[t] -12.5469035036211M2[t] -20.153232530828M3[t] -29.3261851634371M4[t] -23.4958259933451M5[t] -13.0854668232531M6[t] -17.0751076531611M7[t] -15.101436680368M8[t] -2.27438931297713M9[t] -32.760718340184M10[t] -25.890359170092M11[t] -0.330359170091995t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147438&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147438&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 Promet[t] = + 152.062865531415 -18.0165590135056Dummy[t] -2.89726267371302M1[t] -12.5469035036211M2[t] -20.153232530828M3[t] -29.3261851634371M4[t] -23.4958259933451M5[t] -13.0854668232531M6[t] -17.0751076531611M7[t] -15.101436680368M8[t] -2.27438931297713M9[t] -32.760718340184M10[t] -25.890359170092M11[t] -0.330359170091995t + e[t]

 Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 152.062865531415 6.51776 23.3305 0 0 Dummy -18.0165590135056 3.207827 -5.6164 1e-06 1e-06 M1 -2.89726267371302 7.726951 -0.375 0.709417 0.354708 M2 -12.5469035036211 7.716216 -1.626 0.110773 0.055386 M3 -20.153232530828 7.688197 -2.6213 0.011833 0.005917 M4 -29.3261851634371 7.698043 -3.8096 0.000411 0.000206 M5 -23.4958259933451 7.690613 -3.0551 0.003736 0.001868 M6 -13.0854668232531 7.684292 -1.7029 0.095338 0.047669 M7 -17.0751076531611 7.679083 -2.2236 0.031128 0.015564 M8 -15.101436680368 7.651862 -1.9736 0.054456 0.027228 M9 -2.27438931297713 7.672007 -0.2965 0.768219 0.384109 M10 -32.760718340184 7.645135 -4.2852 9.2e-05 4.6e-05 M11 -25.890359170092 7.643452 -3.3873 0.001455 0.000727 t -0.330359170091995 0.092602 -3.5675 0.000855 0.000428

\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) & 152.062865531415 & 6.51776 & 23.3305 & 0 & 0 \tabularnewline
Dummy & -18.0165590135056 & 3.207827 & -5.6164 & 1e-06 & 1e-06 \tabularnewline
M1 & -2.89726267371302 & 7.726951 & -0.375 & 0.709417 & 0.354708 \tabularnewline
M2 & -12.5469035036211 & 7.716216 & -1.626 & 0.110773 & 0.055386 \tabularnewline
M3 & -20.153232530828 & 7.688197 & -2.6213 & 0.011833 & 0.005917 \tabularnewline
M4 & -29.3261851634371 & 7.698043 & -3.8096 & 0.000411 & 0.000206 \tabularnewline
M5 & -23.4958259933451 & 7.690613 & -3.0551 & 0.003736 & 0.001868 \tabularnewline
M6 & -13.0854668232531 & 7.684292 & -1.7029 & 0.095338 & 0.047669 \tabularnewline
M7 & -17.0751076531611 & 7.679083 & -2.2236 & 0.031128 & 0.015564 \tabularnewline
M8 & -15.101436680368 & 7.651862 & -1.9736 & 0.054456 & 0.027228 \tabularnewline
M9 & -2.27438931297713 & 7.672007 & -0.2965 & 0.768219 & 0.384109 \tabularnewline
M10 & -32.760718340184 & 7.645135 & -4.2852 & 9.2e-05 & 4.6e-05 \tabularnewline
M11 & -25.890359170092 & 7.643452 & -3.3873 & 0.001455 & 0.000727 \tabularnewline
t & -0.330359170091995 & 0.092602 & -3.5675 & 0.000855 & 0.000428 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147438&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]152.062865531415[/C][C]6.51776[/C][C]23.3305[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Dummy[/C][C]-18.0165590135056[/C][C]3.207827[/C][C]-5.6164[/C][C]1e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]M1[/C][C]-2.89726267371302[/C][C]7.726951[/C][C]-0.375[/C][C]0.709417[/C][C]0.354708[/C][/ROW]
[ROW][C]M2[/C][C]-12.5469035036211[/C][C]7.716216[/C][C]-1.626[/C][C]0.110773[/C][C]0.055386[/C][/ROW]
[ROW][C]M3[/C][C]-20.153232530828[/C][C]7.688197[/C][C]-2.6213[/C][C]0.011833[/C][C]0.005917[/C][/ROW]
[ROW][C]M4[/C][C]-29.3261851634371[/C][C]7.698043[/C][C]-3.8096[/C][C]0.000411[/C][C]0.000206[/C][/ROW]
[ROW][C]M5[/C][C]-23.4958259933451[/C][C]7.690613[/C][C]-3.0551[/C][C]0.003736[/C][C]0.001868[/C][/ROW]
[ROW][C]M6[/C][C]-13.0854668232531[/C][C]7.684292[/C][C]-1.7029[/C][C]0.095338[/C][C]0.047669[/C][/ROW]
[ROW][C]M7[/C][C]-17.0751076531611[/C][C]7.679083[/C][C]-2.2236[/C][C]0.031128[/C][C]0.015564[/C][/ROW]
[ROW][C]M8[/C][C]-15.101436680368[/C][C]7.651862[/C][C]-1.9736[/C][C]0.054456[/C][C]0.027228[/C][/ROW]
[ROW][C]M9[/C][C]-2.27438931297713[/C][C]7.672007[/C][C]-0.2965[/C][C]0.768219[/C][C]0.384109[/C][/ROW]
[ROW][C]M10[/C][C]-32.760718340184[/C][C]7.645135[/C][C]-4.2852[/C][C]9.2e-05[/C][C]4.6e-05[/C][/ROW]
[ROW][C]M11[/C][C]-25.890359170092[/C][C]7.643452[/C][C]-3.3873[/C][C]0.001455[/C][C]0.000727[/C][/ROW]
[ROW][C]t[/C][C]-0.330359170091995[/C][C]0.092602[/C][C]-3.5675[/C][C]0.000855[/C][C]0.000428[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147438&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147438&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 Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 152.062865531415 6.51776 23.3305 0 0 Dummy -18.0165590135056 3.207827 -5.6164 1e-06 1e-06 M1 -2.89726267371302 7.726951 -0.375 0.709417 0.354708 M2 -12.5469035036211 7.716216 -1.626 0.110773 0.055386 M3 -20.153232530828 7.688197 -2.6213 0.011833 0.005917 M4 -29.3261851634371 7.698043 -3.8096 0.000411 0.000206 M5 -23.4958259933451 7.690613 -3.0551 0.003736 0.001868 M6 -13.0854668232531 7.684292 -1.7029 0.095338 0.047669 M7 -17.0751076531611 7.679083 -2.2236 0.031128 0.015564 M8 -15.101436680368 7.651862 -1.9736 0.054456 0.027228 M9 -2.27438931297713 7.672007 -0.2965 0.768219 0.384109 M10 -32.760718340184 7.645135 -4.2852 9.2e-05 4.6e-05 M11 -25.890359170092 7.643452 -3.3873 0.001455 0.000727 t -0.330359170091995 0.092602 -3.5675 0.000855 0.000428

 Multiple Linear Regression - Regression Statistics Multiple R 0.826784265662317 R-squared 0.683572221946777 Adjusted R-squared 0.59414698032304 F-TEST (value) 7.64406346054904 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 9.7017964995061e-08 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 12.0844718082049 Sum Squared Residuals 6717.5851086318

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.826784265662317 \tabularnewline
R-squared & 0.683572221946777 \tabularnewline
Adjusted R-squared & 0.59414698032304 \tabularnewline
F-TEST (value) & 7.64406346054904 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 46 \tabularnewline
p-value & 9.7017964995061e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 12.0844718082049 \tabularnewline
Sum Squared Residuals & 6717.5851086318 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147438&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.826784265662317[/C][/ROW]
[ROW][C]R-squared[/C][C]0.683572221946777[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]7.64406346054904[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]46[/C][/ROW]
[ROW][C]p-value[/C][C]9.7017964995061e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]12.0844718082049[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]6717.5851086318[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147438&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147438&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 R 0.826784265662317 R-squared 0.683572221946777 Adjusted R-squared 0.59414698032304 F-TEST (value) 7.64406346054904 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 9.7017964995061e-08 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 12.0844718082049 Sum Squared Residuals 6717.5851086318

 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals InterpolationForecast ResidualsPrediction Error 1 112.3 148.83524368761 -36.5352436876098 2 117.3 138.85524368761 -21.5552436876101 3 111.1 112.901996476806 -1.80199647680566 4 102.2 103.398684674105 -1.19868467410453 5 104.3 108.898684674105 -4.59868467410454 6 122.9 118.978684674105 3.92131532589548 7 107.6 114.658684674105 -7.05868467410454 8 121.3 116.301996476806 4.99800352319436 9 131.5 128.798684674105 2.70131532589547 10 89 97.9819964768056 -8.98199647680565 11 104.4 104.521996476806 -0.121996476805641 12 128.9 130.081996476806 -1.18199647680566 13 135.9 126.854374633001 9.04562536699935 14 133.3 116.874374633001 16.4256253669994 15 121.3 108.937686435702 12.3623135642983 16 120.5 117.450933646506 3.04906635349383 17 120.4 122.950933646506 -2.55093364650617 18 137.9 133.030933646506 4.86906635349382 19 126.1 128.710933646506 -2.61093364650618 20 133.2 130.354245449207 2.84575455079269 21 151.1 142.850933646506 8.24906635349382 22 105 112.034245449207 -7.03424544920729 23 119 118.574245449207 0.42575455079271 24 140.4 144.134245449207 -3.73424544920731 25 156.6 140.906623605402 15.6933763945977 26 137.1 130.926623605402 6.17337639459777 27 122.7 122.989935408103 -0.289935408103347 28 125.8 113.486623605402 12.3133763945978 29 139.3 118.986623605402 20.3133763945978 30 134.9 129.066623605402 5.83337639459776 31 149.2 124.746623605402 24.4533763945978 32 132.3 126.389935408103 5.91006459189666 33 149 138.886623605402 10.1133763945978 34 117.2 108.069935408103 9.13006459189666 35 119.6 114.609935408103 4.99006459189665 36 152 140.169935408103 11.8300645918966 37 149.4 136.942313564298 12.4576864357016 38 127.3 126.962313564298 0.337686435701711 39 114.1 119.025625366999 -4.92562536699941 40 102.1 109.522313564298 -7.4223135642983 41 107.7 115.022313564298 -7.3223135642983 42 104.4 125.102313564298 -20.7023135642983 43 102.1 120.782313564298 -18.6823135642983 44 96 104.409066353494 -8.40906635349383 45 109.3 134.922313564298 -25.6223135642983 46 90 86.0890663534938 3.91093364650617 47 83.9 92.6290663534938 -8.72906635349382 48 112 118.189066353494 -6.18906635349383 49 114.3 114.961444509689 -0.66144450968883 50 103.6 104.981444509689 -1.38144450968876 51 91.7 97.0447563123899 -5.34475631238987 52 80.8 87.5414445096888 -6.74144450968877 53 87.2 93.0414445096888 -5.84144450968877 54 109.2 103.121444509689 6.07855549031123 55 102.7 98.8014445096888 3.89855549031124 56 95.1 100.44475631239 -5.34475631238989 57 117.5 112.941444509689 4.55855549031124 58 85.1 82.1247563123899 2.97524368761011 59 92.1 88.6647563123899 3.43524368761011 60 113.5 114.22475631239 -0.724756312389895

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 112.3 & 148.83524368761 & -36.5352436876098 \tabularnewline
2 & 117.3 & 138.85524368761 & -21.5552436876101 \tabularnewline
3 & 111.1 & 112.901996476806 & -1.80199647680566 \tabularnewline
4 & 102.2 & 103.398684674105 & -1.19868467410453 \tabularnewline
5 & 104.3 & 108.898684674105 & -4.59868467410454 \tabularnewline
6 & 122.9 & 118.978684674105 & 3.92131532589548 \tabularnewline
7 & 107.6 & 114.658684674105 & -7.05868467410454 \tabularnewline
8 & 121.3 & 116.301996476806 & 4.99800352319436 \tabularnewline
9 & 131.5 & 128.798684674105 & 2.70131532589547 \tabularnewline
10 & 89 & 97.9819964768056 & -8.98199647680565 \tabularnewline
11 & 104.4 & 104.521996476806 & -0.121996476805641 \tabularnewline
12 & 128.9 & 130.081996476806 & -1.18199647680566 \tabularnewline
13 & 135.9 & 126.854374633001 & 9.04562536699935 \tabularnewline
14 & 133.3 & 116.874374633001 & 16.4256253669994 \tabularnewline
15 & 121.3 & 108.937686435702 & 12.3623135642983 \tabularnewline
16 & 120.5 & 117.450933646506 & 3.04906635349383 \tabularnewline
17 & 120.4 & 122.950933646506 & -2.55093364650617 \tabularnewline
18 & 137.9 & 133.030933646506 & 4.86906635349382 \tabularnewline
19 & 126.1 & 128.710933646506 & -2.61093364650618 \tabularnewline
20 & 133.2 & 130.354245449207 & 2.84575455079269 \tabularnewline
21 & 151.1 & 142.850933646506 & 8.24906635349382 \tabularnewline
22 & 105 & 112.034245449207 & -7.03424544920729 \tabularnewline
23 & 119 & 118.574245449207 & 0.42575455079271 \tabularnewline
24 & 140.4 & 144.134245449207 & -3.73424544920731 \tabularnewline
25 & 156.6 & 140.906623605402 & 15.6933763945977 \tabularnewline
26 & 137.1 & 130.926623605402 & 6.17337639459777 \tabularnewline
27 & 122.7 & 122.989935408103 & -0.289935408103347 \tabularnewline
28 & 125.8 & 113.486623605402 & 12.3133763945978 \tabularnewline
29 & 139.3 & 118.986623605402 & 20.3133763945978 \tabularnewline
30 & 134.9 & 129.066623605402 & 5.83337639459776 \tabularnewline
31 & 149.2 & 124.746623605402 & 24.4533763945978 \tabularnewline
32 & 132.3 & 126.389935408103 & 5.91006459189666 \tabularnewline
33 & 149 & 138.886623605402 & 10.1133763945978 \tabularnewline
34 & 117.2 & 108.069935408103 & 9.13006459189666 \tabularnewline
35 & 119.6 & 114.609935408103 & 4.99006459189665 \tabularnewline
36 & 152 & 140.169935408103 & 11.8300645918966 \tabularnewline
37 & 149.4 & 136.942313564298 & 12.4576864357016 \tabularnewline
38 & 127.3 & 126.962313564298 & 0.337686435701711 \tabularnewline
39 & 114.1 & 119.025625366999 & -4.92562536699941 \tabularnewline
40 & 102.1 & 109.522313564298 & -7.4223135642983 \tabularnewline
41 & 107.7 & 115.022313564298 & -7.3223135642983 \tabularnewline
42 & 104.4 & 125.102313564298 & -20.7023135642983 \tabularnewline
43 & 102.1 & 120.782313564298 & -18.6823135642983 \tabularnewline
44 & 96 & 104.409066353494 & -8.40906635349383 \tabularnewline
45 & 109.3 & 134.922313564298 & -25.6223135642983 \tabularnewline
46 & 90 & 86.0890663534938 & 3.91093364650617 \tabularnewline
47 & 83.9 & 92.6290663534938 & -8.72906635349382 \tabularnewline
48 & 112 & 118.189066353494 & -6.18906635349383 \tabularnewline
49 & 114.3 & 114.961444509689 & -0.66144450968883 \tabularnewline
50 & 103.6 & 104.981444509689 & -1.38144450968876 \tabularnewline
51 & 91.7 & 97.0447563123899 & -5.34475631238987 \tabularnewline
52 & 80.8 & 87.5414445096888 & -6.74144450968877 \tabularnewline
53 & 87.2 & 93.0414445096888 & -5.84144450968877 \tabularnewline
54 & 109.2 & 103.121444509689 & 6.07855549031123 \tabularnewline
55 & 102.7 & 98.8014445096888 & 3.89855549031124 \tabularnewline
56 & 95.1 & 100.44475631239 & -5.34475631238989 \tabularnewline
57 & 117.5 & 112.941444509689 & 4.55855549031124 \tabularnewline
58 & 85.1 & 82.1247563123899 & 2.97524368761011 \tabularnewline
59 & 92.1 & 88.6647563123899 & 3.43524368761011 \tabularnewline
60 & 113.5 & 114.22475631239 & -0.724756312389895 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147438&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]112.3[/C][C]148.83524368761[/C][C]-36.5352436876098[/C][/ROW]
[ROW][C]2[/C][C]117.3[/C][C]138.85524368761[/C][C]-21.5552436876101[/C][/ROW]
[ROW][C]3[/C][C]111.1[/C][C]112.901996476806[/C][C]-1.80199647680566[/C][/ROW]
[ROW][C]4[/C][C]102.2[/C][C]103.398684674105[/C][C]-1.19868467410453[/C][/ROW]
[ROW][C]5[/C][C]104.3[/C][C]108.898684674105[/C][C]-4.59868467410454[/C][/ROW]
[ROW][C]6[/C][C]122.9[/C][C]118.978684674105[/C][C]3.92131532589548[/C][/ROW]
[ROW][C]7[/C][C]107.6[/C][C]114.658684674105[/C][C]-7.05868467410454[/C][/ROW]
[ROW][C]8[/C][C]121.3[/C][C]116.301996476806[/C][C]4.99800352319436[/C][/ROW]
[ROW][C]9[/C][C]131.5[/C][C]128.798684674105[/C][C]2.70131532589547[/C][/ROW]
[ROW][C]10[/C][C]89[/C][C]97.9819964768056[/C][C]-8.98199647680565[/C][/ROW]
[ROW][C]11[/C][C]104.4[/C][C]104.521996476806[/C][C]-0.121996476805641[/C][/ROW]
[ROW][C]12[/C][C]128.9[/C][C]130.081996476806[/C][C]-1.18199647680566[/C][/ROW]
[ROW][C]13[/C][C]135.9[/C][C]126.854374633001[/C][C]9.04562536699935[/C][/ROW]
[ROW][C]14[/C][C]133.3[/C][C]116.874374633001[/C][C]16.4256253669994[/C][/ROW]
[ROW][C]15[/C][C]121.3[/C][C]108.937686435702[/C][C]12.3623135642983[/C][/ROW]
[ROW][C]16[/C][C]120.5[/C][C]117.450933646506[/C][C]3.04906635349383[/C][/ROW]
[ROW][C]17[/C][C]120.4[/C][C]122.950933646506[/C][C]-2.55093364650617[/C][/ROW]
[ROW][C]18[/C][C]137.9[/C][C]133.030933646506[/C][C]4.86906635349382[/C][/ROW]
[ROW][C]19[/C][C]126.1[/C][C]128.710933646506[/C][C]-2.61093364650618[/C][/ROW]
[ROW][C]20[/C][C]133.2[/C][C]130.354245449207[/C][C]2.84575455079269[/C][/ROW]
[ROW][C]21[/C][C]151.1[/C][C]142.850933646506[/C][C]8.24906635349382[/C][/ROW]
[ROW][C]22[/C][C]105[/C][C]112.034245449207[/C][C]-7.03424544920729[/C][/ROW]
[ROW][C]23[/C][C]119[/C][C]118.574245449207[/C][C]0.42575455079271[/C][/ROW]
[ROW][C]24[/C][C]140.4[/C][C]144.134245449207[/C][C]-3.73424544920731[/C][/ROW]
[ROW][C]25[/C][C]156.6[/C][C]140.906623605402[/C][C]15.6933763945977[/C][/ROW]
[ROW][C]26[/C][C]137.1[/C][C]130.926623605402[/C][C]6.17337639459777[/C][/ROW]
[ROW][C]27[/C][C]122.7[/C][C]122.989935408103[/C][C]-0.289935408103347[/C][/ROW]
[ROW][C]28[/C][C]125.8[/C][C]113.486623605402[/C][C]12.3133763945978[/C][/ROW]
[ROW][C]29[/C][C]139.3[/C][C]118.986623605402[/C][C]20.3133763945978[/C][/ROW]
[ROW][C]30[/C][C]134.9[/C][C]129.066623605402[/C][C]5.83337639459776[/C][/ROW]
[ROW][C]31[/C][C]149.2[/C][C]124.746623605402[/C][C]24.4533763945978[/C][/ROW]
[ROW][C]32[/C][C]132.3[/C][C]126.389935408103[/C][C]5.91006459189666[/C][/ROW]
[ROW][C]33[/C][C]149[/C][C]138.886623605402[/C][C]10.1133763945978[/C][/ROW]
[ROW][C]34[/C][C]117.2[/C][C]108.069935408103[/C][C]9.13006459189666[/C][/ROW]
[ROW][C]35[/C][C]119.6[/C][C]114.609935408103[/C][C]4.99006459189665[/C][/ROW]
[ROW][C]36[/C][C]152[/C][C]140.169935408103[/C][C]11.8300645918966[/C][/ROW]
[ROW][C]37[/C][C]149.4[/C][C]136.942313564298[/C][C]12.4576864357016[/C][/ROW]
[ROW][C]38[/C][C]127.3[/C][C]126.962313564298[/C][C]0.337686435701711[/C][/ROW]
[ROW][C]39[/C][C]114.1[/C][C]119.025625366999[/C][C]-4.92562536699941[/C][/ROW]
[ROW][C]40[/C][C]102.1[/C][C]109.522313564298[/C][C]-7.4223135642983[/C][/ROW]
[ROW][C]41[/C][C]107.7[/C][C]115.022313564298[/C][C]-7.3223135642983[/C][/ROW]
[ROW][C]42[/C][C]104.4[/C][C]125.102313564298[/C][C]-20.7023135642983[/C][/ROW]
[ROW][C]43[/C][C]102.1[/C][C]120.782313564298[/C][C]-18.6823135642983[/C][/ROW]
[ROW][C]44[/C][C]96[/C][C]104.409066353494[/C][C]-8.40906635349383[/C][/ROW]
[ROW][C]45[/C][C]109.3[/C][C]134.922313564298[/C][C]-25.6223135642983[/C][/ROW]
[ROW][C]46[/C][C]90[/C][C]86.0890663534938[/C][C]3.91093364650617[/C][/ROW]
[ROW][C]47[/C][C]83.9[/C][C]92.6290663534938[/C][C]-8.72906635349382[/C][/ROW]
[ROW][C]48[/C][C]112[/C][C]118.189066353494[/C][C]-6.18906635349383[/C][/ROW]
[ROW][C]49[/C][C]114.3[/C][C]114.961444509689[/C][C]-0.66144450968883[/C][/ROW]
[ROW][C]50[/C][C]103.6[/C][C]104.981444509689[/C][C]-1.38144450968876[/C][/ROW]
[ROW][C]51[/C][C]91.7[/C][C]97.0447563123899[/C][C]-5.34475631238987[/C][/ROW]
[ROW][C]52[/C][C]80.8[/C][C]87.5414445096888[/C][C]-6.74144450968877[/C][/ROW]
[ROW][C]53[/C][C]87.2[/C][C]93.0414445096888[/C][C]-5.84144450968877[/C][/ROW]
[ROW][C]54[/C][C]109.2[/C][C]103.121444509689[/C][C]6.07855549031123[/C][/ROW]
[ROW][C]55[/C][C]102.7[/C][C]98.8014445096888[/C][C]3.89855549031124[/C][/ROW]
[ROW][C]56[/C][C]95.1[/C][C]100.44475631239[/C][C]-5.34475631238989[/C][/ROW]
[ROW][C]57[/C][C]117.5[/C][C]112.941444509689[/C][C]4.55855549031124[/C][/ROW]
[ROW][C]58[/C][C]85.1[/C][C]82.1247563123899[/C][C]2.97524368761011[/C][/ROW]
[ROW][C]59[/C][C]92.1[/C][C]88.6647563123899[/C][C]3.43524368761011[/C][/ROW]
[ROW][C]60[/C][C]113.5[/C][C]114.22475631239[/C][C]-0.724756312389895[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147438&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147438&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 Index Actuals InterpolationForecast ResidualsPrediction Error 1 112.3 148.83524368761 -36.5352436876098 2 117.3 138.85524368761 -21.5552436876101 3 111.1 112.901996476806 -1.80199647680566 4 102.2 103.398684674105 -1.19868467410453 5 104.3 108.898684674105 -4.59868467410454 6 122.9 118.978684674105 3.92131532589548 7 107.6 114.658684674105 -7.05868467410454 8 121.3 116.301996476806 4.99800352319436 9 131.5 128.798684674105 2.70131532589547 10 89 97.9819964768056 -8.98199647680565 11 104.4 104.521996476806 -0.121996476805641 12 128.9 130.081996476806 -1.18199647680566 13 135.9 126.854374633001 9.04562536699935 14 133.3 116.874374633001 16.4256253669994 15 121.3 108.937686435702 12.3623135642983 16 120.5 117.450933646506 3.04906635349383 17 120.4 122.950933646506 -2.55093364650617 18 137.9 133.030933646506 4.86906635349382 19 126.1 128.710933646506 -2.61093364650618 20 133.2 130.354245449207 2.84575455079269 21 151.1 142.850933646506 8.24906635349382 22 105 112.034245449207 -7.03424544920729 23 119 118.574245449207 0.42575455079271 24 140.4 144.134245449207 -3.73424544920731 25 156.6 140.906623605402 15.6933763945977 26 137.1 130.926623605402 6.17337639459777 27 122.7 122.989935408103 -0.289935408103347 28 125.8 113.486623605402 12.3133763945978 29 139.3 118.986623605402 20.3133763945978 30 134.9 129.066623605402 5.83337639459776 31 149.2 124.746623605402 24.4533763945978 32 132.3 126.389935408103 5.91006459189666 33 149 138.886623605402 10.1133763945978 34 117.2 108.069935408103 9.13006459189666 35 119.6 114.609935408103 4.99006459189665 36 152 140.169935408103 11.8300645918966 37 149.4 136.942313564298 12.4576864357016 38 127.3 126.962313564298 0.337686435701711 39 114.1 119.025625366999 -4.92562536699941 40 102.1 109.522313564298 -7.4223135642983 41 107.7 115.022313564298 -7.3223135642983 42 104.4 125.102313564298 -20.7023135642983 43 102.1 120.782313564298 -18.6823135642983 44 96 104.409066353494 -8.40906635349383 45 109.3 134.922313564298 -25.6223135642983 46 90 86.0890663534938 3.91093364650617 47 83.9 92.6290663534938 -8.72906635349382 48 112 118.189066353494 -6.18906635349383 49 114.3 114.961444509689 -0.66144450968883 50 103.6 104.981444509689 -1.38144450968876 51 91.7 97.0447563123899 -5.34475631238987 52 80.8 87.5414445096888 -6.74144450968877 53 87.2 93.0414445096888 -5.84144450968877 54 109.2 103.121444509689 6.07855549031123 55 102.7 98.8014445096888 3.89855549031124 56 95.1 100.44475631239 -5.34475631238989 57 117.5 112.941444509689 4.55855549031124 58 85.1 82.1247563123899 2.97524368761011 59 92.1 88.6647563123899 3.43524368761011 60 113.5 114.22475631239 -0.724756312389895

 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0555587789641296 0.111117557928259 0.94444122103587 18 0.0154392087324295 0.030878417464859 0.98456079126757 19 0.00528386751060407 0.0105677350212081 0.994716132489396 20 0.00189709242787612 0.00379418485575225 0.998102907572124 21 0.000690905074915974 0.00138181014983195 0.999309094925084 22 0.000253210011032953 0.000506420022065906 0.999746789988967 23 7.06649167315714e-05 0.000141329833463143 0.999929335083268 24 4.57711078581477e-05 9.15422157162954e-05 0.999954228892142 25 7.4563589341809e-05 0.000149127178683618 0.999925436410658 26 0.00088992090144296 0.00177984180288592 0.999110079098557 27 0.00496772944839398 0.00993545889678796 0.995032270551606 28 0.00327411081862902 0.00654822163725803 0.99672588918137 29 0.00277606692689823 0.00555213385379646 0.997223933073102 30 0.00701693388032185 0.0140338677606437 0.992983066119678 31 0.0195759805484197 0.0391519610968394 0.98042401945158 32 0.0323403680798342 0.0646807361596684 0.967659631920166 33 0.0514160710533345 0.102832142106669 0.948583928946665 34 0.0342905082040169 0.0685810164080339 0.965709491795983 35 0.0360785466195566 0.0721570932391131 0.963921453380443 36 0.0777965897031725 0.155593179406345 0.922203410296828 37 0.170263516670527 0.340527033341055 0.829736483329473 38 0.354061258399093 0.708122516798186 0.645938741600907 39 0.544858574796858 0.910282850406285 0.455141425203142 40 0.793289080093725 0.413421839812551 0.206710919906275 41 0.989696390177186 0.0206072196456282 0.0103036098228141 42 0.980171516585623 0.0396569668287548 0.0198284834143774 43 0.964124593924358 0.0717508121512842 0.0358754060756421

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.0555587789641296 & 0.111117557928259 & 0.94444122103587 \tabularnewline
18 & 0.0154392087324295 & 0.030878417464859 & 0.98456079126757 \tabularnewline
19 & 0.00528386751060407 & 0.0105677350212081 & 0.994716132489396 \tabularnewline
20 & 0.00189709242787612 & 0.00379418485575225 & 0.998102907572124 \tabularnewline
21 & 0.000690905074915974 & 0.00138181014983195 & 0.999309094925084 \tabularnewline
22 & 0.000253210011032953 & 0.000506420022065906 & 0.999746789988967 \tabularnewline
23 & 7.06649167315714e-05 & 0.000141329833463143 & 0.999929335083268 \tabularnewline
24 & 4.57711078581477e-05 & 9.15422157162954e-05 & 0.999954228892142 \tabularnewline
25 & 7.4563589341809e-05 & 0.000149127178683618 & 0.999925436410658 \tabularnewline
26 & 0.00088992090144296 & 0.00177984180288592 & 0.999110079098557 \tabularnewline
27 & 0.00496772944839398 & 0.00993545889678796 & 0.995032270551606 \tabularnewline
28 & 0.00327411081862902 & 0.00654822163725803 & 0.99672588918137 \tabularnewline
29 & 0.00277606692689823 & 0.00555213385379646 & 0.997223933073102 \tabularnewline
30 & 0.00701693388032185 & 0.0140338677606437 & 0.992983066119678 \tabularnewline
31 & 0.0195759805484197 & 0.0391519610968394 & 0.98042401945158 \tabularnewline
32 & 0.0323403680798342 & 0.0646807361596684 & 0.967659631920166 \tabularnewline
33 & 0.0514160710533345 & 0.102832142106669 & 0.948583928946665 \tabularnewline
34 & 0.0342905082040169 & 0.0685810164080339 & 0.965709491795983 \tabularnewline
35 & 0.0360785466195566 & 0.0721570932391131 & 0.963921453380443 \tabularnewline
36 & 0.0777965897031725 & 0.155593179406345 & 0.922203410296828 \tabularnewline
37 & 0.170263516670527 & 0.340527033341055 & 0.829736483329473 \tabularnewline
38 & 0.354061258399093 & 0.708122516798186 & 0.645938741600907 \tabularnewline
39 & 0.544858574796858 & 0.910282850406285 & 0.455141425203142 \tabularnewline
40 & 0.793289080093725 & 0.413421839812551 & 0.206710919906275 \tabularnewline
41 & 0.989696390177186 & 0.0206072196456282 & 0.0103036098228141 \tabularnewline
42 & 0.980171516585623 & 0.0396569668287548 & 0.0198284834143774 \tabularnewline
43 & 0.964124593924358 & 0.0717508121512842 & 0.0358754060756421 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147438&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]17[/C][C]0.0555587789641296[/C][C]0.111117557928259[/C][C]0.94444122103587[/C][/ROW]
[ROW][C]18[/C][C]0.0154392087324295[/C][C]0.030878417464859[/C][C]0.98456079126757[/C][/ROW]
[ROW][C]19[/C][C]0.00528386751060407[/C][C]0.0105677350212081[/C][C]0.994716132489396[/C][/ROW]
[ROW][C]20[/C][C]0.00189709242787612[/C][C]0.00379418485575225[/C][C]0.998102907572124[/C][/ROW]
[ROW][C]21[/C][C]0.000690905074915974[/C][C]0.00138181014983195[/C][C]0.999309094925084[/C][/ROW]
[ROW][C]22[/C][C]0.000253210011032953[/C][C]0.000506420022065906[/C][C]0.999746789988967[/C][/ROW]
[ROW][C]23[/C][C]7.06649167315714e-05[/C][C]0.000141329833463143[/C][C]0.999929335083268[/C][/ROW]
[ROW][C]24[/C][C]4.57711078581477e-05[/C][C]9.15422157162954e-05[/C][C]0.999954228892142[/C][/ROW]
[ROW][C]25[/C][C]7.4563589341809e-05[/C][C]0.000149127178683618[/C][C]0.999925436410658[/C][/ROW]
[ROW][C]26[/C][C]0.00088992090144296[/C][C]0.00177984180288592[/C][C]0.999110079098557[/C][/ROW]
[ROW][C]27[/C][C]0.00496772944839398[/C][C]0.00993545889678796[/C][C]0.995032270551606[/C][/ROW]
[ROW][C]28[/C][C]0.00327411081862902[/C][C]0.00654822163725803[/C][C]0.99672588918137[/C][/ROW]
[ROW][C]29[/C][C]0.00277606692689823[/C][C]0.00555213385379646[/C][C]0.997223933073102[/C][/ROW]
[ROW][C]30[/C][C]0.00701693388032185[/C][C]0.0140338677606437[/C][C]0.992983066119678[/C][/ROW]
[ROW][C]31[/C][C]0.0195759805484197[/C][C]0.0391519610968394[/C][C]0.98042401945158[/C][/ROW]
[ROW][C]32[/C][C]0.0323403680798342[/C][C]0.0646807361596684[/C][C]0.967659631920166[/C][/ROW]
[ROW][C]33[/C][C]0.0514160710533345[/C][C]0.102832142106669[/C][C]0.948583928946665[/C][/ROW]
[ROW][C]34[/C][C]0.0342905082040169[/C][C]0.0685810164080339[/C][C]0.965709491795983[/C][/ROW]
[ROW][C]35[/C][C]0.0360785466195566[/C][C]0.0721570932391131[/C][C]0.963921453380443[/C][/ROW]
[ROW][C]36[/C][C]0.0777965897031725[/C][C]0.155593179406345[/C][C]0.922203410296828[/C][/ROW]
[ROW][C]37[/C][C]0.170263516670527[/C][C]0.340527033341055[/C][C]0.829736483329473[/C][/ROW]
[ROW][C]38[/C][C]0.354061258399093[/C][C]0.708122516798186[/C][C]0.645938741600907[/C][/ROW]
[ROW][C]39[/C][C]0.544858574796858[/C][C]0.910282850406285[/C][C]0.455141425203142[/C][/ROW]
[ROW][C]40[/C][C]0.793289080093725[/C][C]0.413421839812551[/C][C]0.206710919906275[/C][/ROW]
[ROW][C]41[/C][C]0.989696390177186[/C][C]0.0206072196456282[/C][C]0.0103036098228141[/C][/ROW]
[ROW][C]42[/C][C]0.980171516585623[/C][C]0.0396569668287548[/C][C]0.0198284834143774[/C][/ROW]
[ROW][C]43[/C][C]0.964124593924358[/C][C]0.0717508121512842[/C][C]0.0358754060756421[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147438&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147438&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-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0555587789641296 0.111117557928259 0.94444122103587 18 0.0154392087324295 0.030878417464859 0.98456079126757 19 0.00528386751060407 0.0105677350212081 0.994716132489396 20 0.00189709242787612 0.00379418485575225 0.998102907572124 21 0.000690905074915974 0.00138181014983195 0.999309094925084 22 0.000253210011032953 0.000506420022065906 0.999746789988967 23 7.06649167315714e-05 0.000141329833463143 0.999929335083268 24 4.57711078581477e-05 9.15422157162954e-05 0.999954228892142 25 7.4563589341809e-05 0.000149127178683618 0.999925436410658 26 0.00088992090144296 0.00177984180288592 0.999110079098557 27 0.00496772944839398 0.00993545889678796 0.995032270551606 28 0.00327411081862902 0.00654822163725803 0.99672588918137 29 0.00277606692689823 0.00555213385379646 0.997223933073102 30 0.00701693388032185 0.0140338677606437 0.992983066119678 31 0.0195759805484197 0.0391519610968394 0.98042401945158 32 0.0323403680798342 0.0646807361596684 0.967659631920166 33 0.0514160710533345 0.102832142106669 0.948583928946665 34 0.0342905082040169 0.0685810164080339 0.965709491795983 35 0.0360785466195566 0.0721570932391131 0.963921453380443 36 0.0777965897031725 0.155593179406345 0.922203410296828 37 0.170263516670527 0.340527033341055 0.829736483329473 38 0.354061258399093 0.708122516798186 0.645938741600907 39 0.544858574796858 0.910282850406285 0.455141425203142 40 0.793289080093725 0.413421839812551 0.206710919906275 41 0.989696390177186 0.0206072196456282 0.0103036098228141 42 0.980171516585623 0.0396569668287548 0.0198284834143774 43 0.964124593924358 0.0717508121512842 0.0358754060756421

 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 10 0.37037037037037 NOK 5% type I error level 16 0.592592592592593 NOK 10% type I error level 20 0.740740740740741 NOK

\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 & 10 & 0.37037037037037 & NOK \tabularnewline
5% type I error level & 16 & 0.592592592592593 & NOK \tabularnewline
10% type I error level & 20 & 0.740740740740741 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147438&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]10[/C][C]0.37037037037037[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]16[/C][C]0.592592592592593[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]20[/C][C]0.740740740740741[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147438&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147438&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 tests OK/NOK 1% type I error level 10 0.37037037037037 NOK 5% type I error level 16 0.592592592592593 NOK 10% type I error level 20 0.740740740740741 NOK

library(lattice)library(lmtest)n25 <- 25 #minimum number of obs. for Goldfeld-Quandt testpar1 <- 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 <- x1if (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'}xk <- length(x[1,])df <- as.data.frame(x)(mylm <- lm(df))(mysum <- summary(mylm))if (n > n25) {kp3 <- k + 3nmkm3 <- n - k - 3gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))numgqtests <- 0numsignificant1 <- 0numsignificant5 <- 0numsignificant10 <- 0for (mypoint in kp3:nmkm3) {j <- 0numgqtests <- numgqtests + 1for (myalt in c('greater', 'two.sided', 'less')) {j <- j + 1gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value}if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1}gqarr}bitmap(file='test0.png')plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')points(x[,1]-mysum$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)dumdum1 <- dum[2:length(myerror),]dum1z <- as.data.frame(dum1)zplot(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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')}}myeq <- paste(myeq, ' + e[t]')a<-table.row.start(a)a<-table.element(a, myeq)a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable1.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'Variable',header=TRUE)a<-table.element(a,'Parameter',header=TRUE)a<-table.element(a,'S.D.',header=TRUE)a<-table.element(a,'T-STATH0: parameter = 0',header=TRUE)a<-table.element(a,'2-tail p-value',header=TRUE)a<-table.element(a,'1-tail p-value',header=TRUE)a<-table.row.end(a)for (i in 1:k){a<-table.row.start(a)a<-table.element(a,rownames(mysum$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$sigma)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Sum Squared Residuals',1,TRUE)a<-table.element(a, sum(myerror*myerror))a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable3.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Time or Index', 1, TRUE)a<-table.element(a, 'Actuals', 1, TRUE)a<-table.element(a, 'InterpolationForecast', 1, TRUE)a<-table.element(a, 'ResidualsPrediction Error', 1, TRUE)a<-table.row.end(a)for (i in 1:n) {a<-table.row.start(a)a<-table.element(a,i, 1, TRUE)a<-table.element(a,x[i])a<-table.element(a,x[i]-mysum$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')}