## 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 13:32:22 -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/t1322332393g1q2pwkgtfllmgr.htm/, Retrieved Mon, 30 Jan 2023 01:48:54 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147440, Retrieved Mon, 30 Jan 2023 01:48:54 +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 14:10:54] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [Multivariate regr...] [2009-11-19 09:46:44] [21324e9cdf3569788a3d630236984d87]
-    D      [Multiple Regression] [] [2010-12-07 13:11:12] [f47feae0308dca73181bb669fbad1c56]
- R             [Multiple Regression] [] [2011-11-26 18:32:22] [d41d8cd98f00b204e9800998ecf8427e] [Current]
- R P             [Multiple Regression] [] [2011-11-27 16:58:04] [3931071255a6f7f4a767409781cc5f7d]
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Dataseries X:
112.3	0	117.2	96.8
117.3	0	112.3	117.2
111.1	1	117.3	112.3
102.2	1	111.1	117.3
104.3	1	102.2	111.1
122.9	1	104.3	102.2
107.6	1	122.9	104.3
121.3	1	107.6	122.9
131.5	1	121.3	107.6
89	1	131.5	121.3
104.4	1	89	131.5
128.9	1	104.4	89
135.9	1	128.9	104.4
133.3	1	135.9	128.9
121.3	1	133.3	135.9
120.5	0	121.3	133.3
120.4	0	120.5	121.3
137.9	0	120.4	120.5
126.1	0	137.9	120.4
133.2	0	126.1	137.9
151.1	0	133.2	126.1
105	0	151.1	133.2
119	0	105	151.1
140.4	0	119	105
156.6	0	140.4	119
137.1	0	156.6	140.4
122.7	0	137.1	156.6
125.8	0	122.7	137.1
139.3	0	125.8	122.7
134.9	0	139.3	125.8
149.2	0	134.9	139.3
132.3	0	149.2	134.9
149	0	132.3	149.2
117.2	0	149	132.3
119.6	0	117.2	149
152	0	119.6	117.2
149.4	0	152	119.6
127.3	0	149.4	152
114.1	0	127.3	149.4
102.1	0	114.1	127.3
107.7	0	102.1	114.1
104.4	0	107.7	102.1
102.1	0	104.4	107.7
96	1	102.1	104.4
109.3	0	96	102.1
90	1	109.3	96
83.9	1	90	109.3
112	1	83.9	90
114.3	1	112	83.9
103.6	1	114.3	112
91.7	1	103.6	114.3
80.8	1	91.7	103.6
87.2	1	80.8	91.7
109.2	1	87.2	80.8
102.7	1	109.2	87.2
95.1	1	102.7	109.2
117.5	1	95.1	102.7
85.1	1	117.5	95.1
92.1	1	85.1	117.5
113.5	1	92.1	85.1


 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 4 seconds R Server 'Gwilym Jenkins' @ jenkins.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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147440&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' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147440&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147440&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 4 seconds R Server 'Gwilym Jenkins' @ jenkins.wessa.net

 Multiple Linear Regression - Estimated Regression Equation X[t] = + 39.5718545572898 + 1.4428225092746Y[t] + 0.462651817415592y(t)[t] + 0.456836619187371y(t-1)[t] -12.0357634082612M1[t] -35.1685550498614M2[t] -43.9263346792956M3[t] -39.5414071812871M4[t] -25.9417353094251M5[t] -15.6128526883177M6[t] -27.0108528497098M7[t] -31.7675430580503M8[t] -12.4005152354837M9[t] -53.5642426604347M10[t] -38.356705110219M11[t] -0.0981315636876872t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
X[t] =  +  39.5718545572898 +  1.4428225092746Y[t] +  0.462651817415592y(t)[t] +  0.456836619187371y(t-1)[t] -12.0357634082612M1[t] -35.1685550498614M2[t] -43.9263346792956M3[t] -39.5414071812871M4[t] -25.9417353094251M5[t] -15.6128526883177M6[t] -27.0108528497098M7[t] -31.7675430580503M8[t] -12.4005152354837M9[t] -53.5642426604347M10[t] -38.356705110219M11[t] -0.0981315636876872t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147440&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]X[t] =  +  39.5718545572898 +  1.4428225092746Y[t] +  0.462651817415592y(t)[t] +  0.456836619187371y(t-1)[t] -12.0357634082612M1[t] -35.1685550498614M2[t] -43.9263346792956M3[t] -39.5414071812871M4[t] -25.9417353094251M5[t] -15.6128526883177M6[t] -27.0108528497098M7[t] -31.7675430580503M8[t] -12.4005152354837M9[t] -53.5642426604347M10[t] -38.356705110219M11[t] -0.0981315636876872t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147440&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147440&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 X[t] = + 39.5718545572898 + 1.4428225092746Y[t] + 0.462651817415592y(t)[t] + 0.456836619187371y(t-1)[t] -12.0357634082612M1[t] -35.1685550498614M2[t] -43.9263346792956M3[t] -39.5414071812871M4[t] -25.9417353094251M5[t] -15.6128526883177M6[t] -27.0108528497098M7[t] -31.7675430580503M8[t] -12.4005152354837M9[t] -53.5642426604347M10[t] -38.356705110219M11[t] -0.0981315636876872t + 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) 39.5718545572898 14.916987 2.6528 0.011062 0.005531 Y 1.4428225092746 3.352062 0.4304 0.668985 0.334493 y(t) 0.462651817415592 0.134518 3.4393 0.001287 0.000644 y(t-1) 0.456836619187371 0.155403 2.9397 0.005218 0.002609 M1 -12.0357634082612 5.673619 -2.1214 0.039562 0.019781 M2 -35.1685550498614 5.876229 -5.9849 0 0 M3 -43.9263346792956 6.513124 -6.7443 0 0 M4 -39.5414071812871 5.828352 -6.7843 0 0 M5 -25.9417353094251 5.317747 -4.8783 1.4e-05 7e-06 M6 -15.6128526883177 5.046647 -3.0937 0.003429 0.001715 M7 -27.0108528497098 5.191112 -5.2033 5e-06 2e-06 M8 -31.7675430580503 5.75193 -5.5229 2e-06 1e-06 M9 -12.4005152354837 5.350115 -2.3178 0.025172 0.012586 M10 -53.5642426604347 5.698954 -9.399 0 0 M11 -38.356705110219 7.794011 -4.9213 1.2e-05 6e-06 t -0.0981315636876872 0.067336 -1.4573 0.152124 0.076062

\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) & 39.5718545572898 & 14.916987 & 2.6528 & 0.011062 & 0.005531 \tabularnewline
Y & 1.4428225092746 & 3.352062 & 0.4304 & 0.668985 & 0.334493 \tabularnewline
y(t) & 0.462651817415592 & 0.134518 & 3.4393 & 0.001287 & 0.000644 \tabularnewline
y(t-1) & 0.456836619187371 & 0.155403 & 2.9397 & 0.005218 & 0.002609 \tabularnewline
M1 & -12.0357634082612 & 5.673619 & -2.1214 & 0.039562 & 0.019781 \tabularnewline
M2 & -35.1685550498614 & 5.876229 & -5.9849 & 0 & 0 \tabularnewline
M3 & -43.9263346792956 & 6.513124 & -6.7443 & 0 & 0 \tabularnewline
M4 & -39.5414071812871 & 5.828352 & -6.7843 & 0 & 0 \tabularnewline
M5 & -25.9417353094251 & 5.317747 & -4.8783 & 1.4e-05 & 7e-06 \tabularnewline
M6 & -15.6128526883177 & 5.046647 & -3.0937 & 0.003429 & 0.001715 \tabularnewline
M7 & -27.0108528497098 & 5.191112 & -5.2033 & 5e-06 & 2e-06 \tabularnewline
M8 & -31.7675430580503 & 5.75193 & -5.5229 & 2e-06 & 1e-06 \tabularnewline
M9 & -12.4005152354837 & 5.350115 & -2.3178 & 0.025172 & 0.012586 \tabularnewline
M10 & -53.5642426604347 & 5.698954 & -9.399 & 0 & 0 \tabularnewline
M11 & -38.356705110219 & 7.794011 & -4.9213 & 1.2e-05 & 6e-06 \tabularnewline
t & -0.0981315636876872 & 0.067336 & -1.4573 & 0.152124 & 0.076062 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147440&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]39.5718545572898[/C][C]14.916987[/C][C]2.6528[/C][C]0.011062[/C][C]0.005531[/C][/ROW]
[ROW][C]Y[/C][C]1.4428225092746[/C][C]3.352062[/C][C]0.4304[/C][C]0.668985[/C][C]0.334493[/C][/ROW]
[ROW][C]y(t)[/C][C]0.462651817415592[/C][C]0.134518[/C][C]3.4393[/C][C]0.001287[/C][C]0.000644[/C][/ROW]
[ROW][C]y(t-1)[/C][C]0.456836619187371[/C][C]0.155403[/C][C]2.9397[/C][C]0.005218[/C][C]0.002609[/C][/ROW]
[ROW][C]M1[/C][C]-12.0357634082612[/C][C]5.673619[/C][C]-2.1214[/C][C]0.039562[/C][C]0.019781[/C][/ROW]
[ROW][C]M2[/C][C]-35.1685550498614[/C][C]5.876229[/C][C]-5.9849[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M3[/C][C]-43.9263346792956[/C][C]6.513124[/C][C]-6.7443[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M4[/C][C]-39.5414071812871[/C][C]5.828352[/C][C]-6.7843[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M5[/C][C]-25.9417353094251[/C][C]5.317747[/C][C]-4.8783[/C][C]1.4e-05[/C][C]7e-06[/C][/ROW]
[ROW][C]M6[/C][C]-15.6128526883177[/C][C]5.046647[/C][C]-3.0937[/C][C]0.003429[/C][C]0.001715[/C][/ROW]
[ROW][C]M7[/C][C]-27.0108528497098[/C][C]5.191112[/C][C]-5.2033[/C][C]5e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]M8[/C][C]-31.7675430580503[/C][C]5.75193[/C][C]-5.5229[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]M9[/C][C]-12.4005152354837[/C][C]5.350115[/C][C]-2.3178[/C][C]0.025172[/C][C]0.012586[/C][/ROW]
[ROW][C]M10[/C][C]-53.5642426604347[/C][C]5.698954[/C][C]-9.399[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M11[/C][C]-38.356705110219[/C][C]7.794011[/C][C]-4.9213[/C][C]1.2e-05[/C][C]6e-06[/C][/ROW]
[ROW][C]t[/C][C]-0.0981315636876872[/C][C]0.067336[/C][C]-1.4573[/C][C]0.152124[/C][C]0.076062[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147440&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147440&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) 39.5718545572898 14.916987 2.6528 0.011062 0.005531 Y 1.4428225092746 3.352062 0.4304 0.668985 0.334493 y(t) 0.462651817415592 0.134518 3.4393 0.001287 0.000644 y(t-1) 0.456836619187371 0.155403 2.9397 0.005218 0.002609 M1 -12.0357634082612 5.673619 -2.1214 0.039562 0.019781 M2 -35.1685550498614 5.876229 -5.9849 0 0 M3 -43.9263346792956 6.513124 -6.7443 0 0 M4 -39.5414071812871 5.828352 -6.7843 0 0 M5 -25.9417353094251 5.317747 -4.8783 1.4e-05 7e-06 M6 -15.6128526883177 5.046647 -3.0937 0.003429 0.001715 M7 -27.0108528497098 5.191112 -5.2033 5e-06 2e-06 M8 -31.7675430580503 5.75193 -5.5229 2e-06 1e-06 M9 -12.4005152354837 5.350115 -2.3178 0.025172 0.012586 M10 -53.5642426604347 5.698954 -9.399 0 0 M11 -38.356705110219 7.794011 -4.9213 1.2e-05 6e-06 t -0.0981315636876872 0.067336 -1.4573 0.152124 0.076062

 Multiple Linear Regression - Regression Statistics Multiple R 0.932787352234213 R-squared 0.870092244488114 Adjusted R-squared 0.825805509654517 F-TEST (value) 19.6467914773439 F-TEST (DF numerator) 15 F-TEST (DF denominator) 44 p-value 1.04360964314765e-14 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 7.91699716280575 Sum Squared Residuals 2757.86913933847

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.932787352234213 \tabularnewline
R-squared & 0.870092244488114 \tabularnewline
F-TEST (value) & 19.6467914773439 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 44 \tabularnewline
p-value & 1.04360964314765e-14 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 7.91699716280575 \tabularnewline
Sum Squared Residuals & 2757.86913933847 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147440&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.932787352234213[/C][/ROW]
[ROW][C]R-squared[/C][C]0.870092244488114[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]19.6467914773439[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]44[/C][/ROW]
[ROW][C]p-value[/C][C]1.04360964314765e-14[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]7.91699716280575[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2757.86913933847[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147440&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147440&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 0.932787352234213 R-squared 0.870092244488114 Adjusted R-squared 0.825805509654517 F-TEST (value) 19.6467914773439 F-TEST (DF numerator) 15 F-TEST (DF denominator) 44 p-value 1.04360964314765e-14 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 7.91699716280575 Sum Squared Residuals 2757.86913933847

 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals InterpolationForecast ResidualsPrediction Error 1 112.3 125.882537323786 -13.5825373237857 2 117.3 109.704087244584 7.5959127554161 3 111.1 102.365758213796 8.73424178620351 4 102.2 106.068295976077 -3.86829597607748 5 104.3 112.619848070291 -8.31984807029128 6 122.9 119.756322033516 3.14367796648385 7 107.6 117.82487101266 -10.2248710126599 8 121.3 114.388637551058 6.91136244894178 9 131.5 133.006263434964 -1.50626343496396 10 89 102.722114666831 -13.7221146668313 11 104.4 102.828551928908 1.57144807109218 12 128.9 128.796407148176 0.103592851824 13 135.9 135.032765638395 0.867234361605378 14 133.3 126.232902325106 7.06709767489355 15 121.3 119.371952741016 1.92804725898431 16 120.5 115.476329147188 5.02367085281238 17 120.4 123.125708571181 -2.72570857118096 18 137.9 132.944725151509 4.95527484849077 19 126.1 129.499316569284 -3.39931656928365 20 133.2 127.17984418753 6.0201558124696 21 151.1 144.342896243649 6.75710375635096 22 105 114.60604478298 -9.60604478297976 23 119 116.564577470103 2.43542252989708 24 140.4 140.240108315915 0.159891684085257 25 156.6 144.402674905283 12.1973250947173 26 137.1 138.443014792737 -1.34301479273715 27 122.7 127.966146390847 -5.26614639084666 28 125.8 116.682442080229 9.11755791977077 29 139.3 125.039755706094 14.2602442939063 30 134.9 142.932499818105 -8.03249981810474 31 149.2 135.567994455426 13.6320055445741 32 132.3 135.319012548016 -3.01901254801619 33 149 153.301856746951 -4.30185674695104 34 117.2 112.045744244886 5.15425575511385 35 119.6 120.071993978027 -0.471993978027431 36 152 144.913527396198 7.08647260380221 37 149.4 148.865959194564 0.534040805436261 38 127.3 139.233647725666 -11.9336477256661 39 114.1 118.965356157773 -4.86535615777254 40 102.1 107.049058818167 -4.94905881816664 41 107.7 108.96853394408 -1.26853394408049 42 104.4 116.308095748779 -11.9080957487791 43 102.1 105.843498093677 -3.7434980936772 44 96 99.8598388075494 -3.85983880754935 45 109.3 113.813012246788 -4.51301224678761 46 90 77.3605415620079 12.6394584379921 47 83.9 89.616694507607 -5.71669450760702 48 112 116.236145217587 -4.23614521758701 49 114.3 114.316062937973 -0.0160629379732617 50 103.6 104.986347911906 -1.38634791190635 51 91.7 92.2307864965686 -0.530786496568621 52 80.8 86.123873978339 -5.32387397833903 53 87.2 89.1461537083536 -1.94615370835361 54 109.2 97.3583572480908 11.8416427519092 55 102.7 98.9643198689533 3.73568013104673 56 95.1 101.152666905846 -6.05266690584585 57 117.5 113.935971327648 3.56402867235165 58 85.1 79.5655547432949 5.53444525670511 59 92.1 89.9181821153548 2.18181788464518 60 113.5 116.613811922124 -3.11381192212449

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 112.3 & 125.882537323786 & -13.5825373237857 \tabularnewline
2 & 117.3 & 109.704087244584 & 7.5959127554161 \tabularnewline
3 & 111.1 & 102.365758213796 & 8.73424178620351 \tabularnewline
4 & 102.2 & 106.068295976077 & -3.86829597607748 \tabularnewline
5 & 104.3 & 112.619848070291 & -8.31984807029128 \tabularnewline
6 & 122.9 & 119.756322033516 & 3.14367796648385 \tabularnewline
7 & 107.6 & 117.82487101266 & -10.2248710126599 \tabularnewline
8 & 121.3 & 114.388637551058 & 6.91136244894178 \tabularnewline
9 & 131.5 & 133.006263434964 & -1.50626343496396 \tabularnewline
10 & 89 & 102.722114666831 & -13.7221146668313 \tabularnewline
11 & 104.4 & 102.828551928908 & 1.57144807109218 \tabularnewline
12 & 128.9 & 128.796407148176 & 0.103592851824 \tabularnewline
13 & 135.9 & 135.032765638395 & 0.867234361605378 \tabularnewline
14 & 133.3 & 126.232902325106 & 7.06709767489355 \tabularnewline
15 & 121.3 & 119.371952741016 & 1.92804725898431 \tabularnewline
16 & 120.5 & 115.476329147188 & 5.02367085281238 \tabularnewline
17 & 120.4 & 123.125708571181 & -2.72570857118096 \tabularnewline
18 & 137.9 & 132.944725151509 & 4.95527484849077 \tabularnewline
19 & 126.1 & 129.499316569284 & -3.39931656928365 \tabularnewline
20 & 133.2 & 127.17984418753 & 6.0201558124696 \tabularnewline
21 & 151.1 & 144.342896243649 & 6.75710375635096 \tabularnewline
22 & 105 & 114.60604478298 & -9.60604478297976 \tabularnewline
23 & 119 & 116.564577470103 & 2.43542252989708 \tabularnewline
24 & 140.4 & 140.240108315915 & 0.159891684085257 \tabularnewline
25 & 156.6 & 144.402674905283 & 12.1973250947173 \tabularnewline
26 & 137.1 & 138.443014792737 & -1.34301479273715 \tabularnewline
27 & 122.7 & 127.966146390847 & -5.26614639084666 \tabularnewline
28 & 125.8 & 116.682442080229 & 9.11755791977077 \tabularnewline
29 & 139.3 & 125.039755706094 & 14.2602442939063 \tabularnewline
30 & 134.9 & 142.932499818105 & -8.03249981810474 \tabularnewline
31 & 149.2 & 135.567994455426 & 13.6320055445741 \tabularnewline
32 & 132.3 & 135.319012548016 & -3.01901254801619 \tabularnewline
33 & 149 & 153.301856746951 & -4.30185674695104 \tabularnewline
34 & 117.2 & 112.045744244886 & 5.15425575511385 \tabularnewline
35 & 119.6 & 120.071993978027 & -0.471993978027431 \tabularnewline
36 & 152 & 144.913527396198 & 7.08647260380221 \tabularnewline
37 & 149.4 & 148.865959194564 & 0.534040805436261 \tabularnewline
38 & 127.3 & 139.233647725666 & -11.9336477256661 \tabularnewline
39 & 114.1 & 118.965356157773 & -4.86535615777254 \tabularnewline
40 & 102.1 & 107.049058818167 & -4.94905881816664 \tabularnewline
41 & 107.7 & 108.96853394408 & -1.26853394408049 \tabularnewline
42 & 104.4 & 116.308095748779 & -11.9080957487791 \tabularnewline
43 & 102.1 & 105.843498093677 & -3.7434980936772 \tabularnewline
44 & 96 & 99.8598388075494 & -3.85983880754935 \tabularnewline
45 & 109.3 & 113.813012246788 & -4.51301224678761 \tabularnewline
46 & 90 & 77.3605415620079 & 12.6394584379921 \tabularnewline
47 & 83.9 & 89.616694507607 & -5.71669450760702 \tabularnewline
48 & 112 & 116.236145217587 & -4.23614521758701 \tabularnewline
49 & 114.3 & 114.316062937973 & -0.0160629379732617 \tabularnewline
50 & 103.6 & 104.986347911906 & -1.38634791190635 \tabularnewline
51 & 91.7 & 92.2307864965686 & -0.530786496568621 \tabularnewline
52 & 80.8 & 86.123873978339 & -5.32387397833903 \tabularnewline
53 & 87.2 & 89.1461537083536 & -1.94615370835361 \tabularnewline
54 & 109.2 & 97.3583572480908 & 11.8416427519092 \tabularnewline
55 & 102.7 & 98.9643198689533 & 3.73568013104673 \tabularnewline
56 & 95.1 & 101.152666905846 & -6.05266690584585 \tabularnewline
57 & 117.5 & 113.935971327648 & 3.56402867235165 \tabularnewline
58 & 85.1 & 79.5655547432949 & 5.53444525670511 \tabularnewline
59 & 92.1 & 89.9181821153548 & 2.18181788464518 \tabularnewline
60 & 113.5 & 116.613811922124 & -3.11381192212449 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147440&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]125.882537323786[/C][C]-13.5825373237857[/C][/ROW]
[ROW][C]2[/C][C]117.3[/C][C]109.704087244584[/C][C]7.5959127554161[/C][/ROW]
[ROW][C]3[/C][C]111.1[/C][C]102.365758213796[/C][C]8.73424178620351[/C][/ROW]
[ROW][C]4[/C][C]102.2[/C][C]106.068295976077[/C][C]-3.86829597607748[/C][/ROW]
[ROW][C]5[/C][C]104.3[/C][C]112.619848070291[/C][C]-8.31984807029128[/C][/ROW]
[ROW][C]6[/C][C]122.9[/C][C]119.756322033516[/C][C]3.14367796648385[/C][/ROW]
[ROW][C]7[/C][C]107.6[/C][C]117.82487101266[/C][C]-10.2248710126599[/C][/ROW]
[ROW][C]8[/C][C]121.3[/C][C]114.388637551058[/C][C]6.91136244894178[/C][/ROW]
[ROW][C]9[/C][C]131.5[/C][C]133.006263434964[/C][C]-1.50626343496396[/C][/ROW]
[ROW][C]10[/C][C]89[/C][C]102.722114666831[/C][C]-13.7221146668313[/C][/ROW]
[ROW][C]11[/C][C]104.4[/C][C]102.828551928908[/C][C]1.57144807109218[/C][/ROW]
[ROW][C]12[/C][C]128.9[/C][C]128.796407148176[/C][C]0.103592851824[/C][/ROW]
[ROW][C]13[/C][C]135.9[/C][C]135.032765638395[/C][C]0.867234361605378[/C][/ROW]
[ROW][C]14[/C][C]133.3[/C][C]126.232902325106[/C][C]7.06709767489355[/C][/ROW]
[ROW][C]15[/C][C]121.3[/C][C]119.371952741016[/C][C]1.92804725898431[/C][/ROW]
[ROW][C]16[/C][C]120.5[/C][C]115.476329147188[/C][C]5.02367085281238[/C][/ROW]
[ROW][C]17[/C][C]120.4[/C][C]123.125708571181[/C][C]-2.72570857118096[/C][/ROW]
[ROW][C]18[/C][C]137.9[/C][C]132.944725151509[/C][C]4.95527484849077[/C][/ROW]
[ROW][C]19[/C][C]126.1[/C][C]129.499316569284[/C][C]-3.39931656928365[/C][/ROW]
[ROW][C]20[/C][C]133.2[/C][C]127.17984418753[/C][C]6.0201558124696[/C][/ROW]
[ROW][C]21[/C][C]151.1[/C][C]144.342896243649[/C][C]6.75710375635096[/C][/ROW]
[ROW][C]22[/C][C]105[/C][C]114.60604478298[/C][C]-9.60604478297976[/C][/ROW]
[ROW][C]23[/C][C]119[/C][C]116.564577470103[/C][C]2.43542252989708[/C][/ROW]
[ROW][C]24[/C][C]140.4[/C][C]140.240108315915[/C][C]0.159891684085257[/C][/ROW]
[ROW][C]25[/C][C]156.6[/C][C]144.402674905283[/C][C]12.1973250947173[/C][/ROW]
[ROW][C]26[/C][C]137.1[/C][C]138.443014792737[/C][C]-1.34301479273715[/C][/ROW]
[ROW][C]27[/C][C]122.7[/C][C]127.966146390847[/C][C]-5.26614639084666[/C][/ROW]
[ROW][C]28[/C][C]125.8[/C][C]116.682442080229[/C][C]9.11755791977077[/C][/ROW]
[ROW][C]29[/C][C]139.3[/C][C]125.039755706094[/C][C]14.2602442939063[/C][/ROW]
[ROW][C]30[/C][C]134.9[/C][C]142.932499818105[/C][C]-8.03249981810474[/C][/ROW]
[ROW][C]31[/C][C]149.2[/C][C]135.567994455426[/C][C]13.6320055445741[/C][/ROW]
[ROW][C]32[/C][C]132.3[/C][C]135.319012548016[/C][C]-3.01901254801619[/C][/ROW]
[ROW][C]33[/C][C]149[/C][C]153.301856746951[/C][C]-4.30185674695104[/C][/ROW]
[ROW][C]34[/C][C]117.2[/C][C]112.045744244886[/C][C]5.15425575511385[/C][/ROW]
[ROW][C]35[/C][C]119.6[/C][C]120.071993978027[/C][C]-0.471993978027431[/C][/ROW]
[ROW][C]36[/C][C]152[/C][C]144.913527396198[/C][C]7.08647260380221[/C][/ROW]
[ROW][C]37[/C][C]149.4[/C][C]148.865959194564[/C][C]0.534040805436261[/C][/ROW]
[ROW][C]38[/C][C]127.3[/C][C]139.233647725666[/C][C]-11.9336477256661[/C][/ROW]
[ROW][C]39[/C][C]114.1[/C][C]118.965356157773[/C][C]-4.86535615777254[/C][/ROW]
[ROW][C]40[/C][C]102.1[/C][C]107.049058818167[/C][C]-4.94905881816664[/C][/ROW]
[ROW][C]41[/C][C]107.7[/C][C]108.96853394408[/C][C]-1.26853394408049[/C][/ROW]
[ROW][C]42[/C][C]104.4[/C][C]116.308095748779[/C][C]-11.9080957487791[/C][/ROW]
[ROW][C]43[/C][C]102.1[/C][C]105.843498093677[/C][C]-3.7434980936772[/C][/ROW]
[ROW][C]44[/C][C]96[/C][C]99.8598388075494[/C][C]-3.85983880754935[/C][/ROW]
[ROW][C]45[/C][C]109.3[/C][C]113.813012246788[/C][C]-4.51301224678761[/C][/ROW]
[ROW][C]46[/C][C]90[/C][C]77.3605415620079[/C][C]12.6394584379921[/C][/ROW]
[ROW][C]47[/C][C]83.9[/C][C]89.616694507607[/C][C]-5.71669450760702[/C][/ROW]
[ROW][C]48[/C][C]112[/C][C]116.236145217587[/C][C]-4.23614521758701[/C][/ROW]
[ROW][C]49[/C][C]114.3[/C][C]114.316062937973[/C][C]-0.0160629379732617[/C][/ROW]
[ROW][C]50[/C][C]103.6[/C][C]104.986347911906[/C][C]-1.38634791190635[/C][/ROW]
[ROW][C]51[/C][C]91.7[/C][C]92.2307864965686[/C][C]-0.530786496568621[/C][/ROW]
[ROW][C]52[/C][C]80.8[/C][C]86.123873978339[/C][C]-5.32387397833903[/C][/ROW]
[ROW][C]53[/C][C]87.2[/C][C]89.1461537083536[/C][C]-1.94615370835361[/C][/ROW]
[ROW][C]54[/C][C]109.2[/C][C]97.3583572480908[/C][C]11.8416427519092[/C][/ROW]
[ROW][C]55[/C][C]102.7[/C][C]98.9643198689533[/C][C]3.73568013104673[/C][/ROW]
[ROW][C]56[/C][C]95.1[/C][C]101.152666905846[/C][C]-6.05266690584585[/C][/ROW]
[ROW][C]57[/C][C]117.5[/C][C]113.935971327648[/C][C]3.56402867235165[/C][/ROW]
[ROW][C]58[/C][C]85.1[/C][C]79.5655547432949[/C][C]5.53444525670511[/C][/ROW]
[ROW][C]59[/C][C]92.1[/C][C]89.9181821153548[/C][C]2.18181788464518[/C][/ROW]
[ROW][C]60[/C][C]113.5[/C][C]116.613811922124[/C][C]-3.11381192212449[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147440&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147440&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 1 112.3 125.882537323786 -13.5825373237857 2 117.3 109.704087244584 7.5959127554161 3 111.1 102.365758213796 8.73424178620351 4 102.2 106.068295976077 -3.86829597607748 5 104.3 112.619848070291 -8.31984807029128 6 122.9 119.756322033516 3.14367796648385 7 107.6 117.82487101266 -10.2248710126599 8 121.3 114.388637551058 6.91136244894178 9 131.5 133.006263434964 -1.50626343496396 10 89 102.722114666831 -13.7221146668313 11 104.4 102.828551928908 1.57144807109218 12 128.9 128.796407148176 0.103592851824 13 135.9 135.032765638395 0.867234361605378 14 133.3 126.232902325106 7.06709767489355 15 121.3 119.371952741016 1.92804725898431 16 120.5 115.476329147188 5.02367085281238 17 120.4 123.125708571181 -2.72570857118096 18 137.9 132.944725151509 4.95527484849077 19 126.1 129.499316569284 -3.39931656928365 20 133.2 127.17984418753 6.0201558124696 21 151.1 144.342896243649 6.75710375635096 22 105 114.60604478298 -9.60604478297976 23 119 116.564577470103 2.43542252989708 24 140.4 140.240108315915 0.159891684085257 25 156.6 144.402674905283 12.1973250947173 26 137.1 138.443014792737 -1.34301479273715 27 122.7 127.966146390847 -5.26614639084666 28 125.8 116.682442080229 9.11755791977077 29 139.3 125.039755706094 14.2602442939063 30 134.9 142.932499818105 -8.03249981810474 31 149.2 135.567994455426 13.6320055445741 32 132.3 135.319012548016 -3.01901254801619 33 149 153.301856746951 -4.30185674695104 34 117.2 112.045744244886 5.15425575511385 35 119.6 120.071993978027 -0.471993978027431 36 152 144.913527396198 7.08647260380221 37 149.4 148.865959194564 0.534040805436261 38 127.3 139.233647725666 -11.9336477256661 39 114.1 118.965356157773 -4.86535615777254 40 102.1 107.049058818167 -4.94905881816664 41 107.7 108.96853394408 -1.26853394408049 42 104.4 116.308095748779 -11.9080957487791 43 102.1 105.843498093677 -3.7434980936772 44 96 99.8598388075494 -3.85983880754935 45 109.3 113.813012246788 -4.51301224678761 46 90 77.3605415620079 12.6394584379921 47 83.9 89.616694507607 -5.71669450760702 48 112 116.236145217587 -4.23614521758701 49 114.3 114.316062937973 -0.0160629379732617 50 103.6 104.986347911906 -1.38634791190635 51 91.7 92.2307864965686 -0.530786496568621 52 80.8 86.123873978339 -5.32387397833903 53 87.2 89.1461537083536 -1.94615370835361 54 109.2 97.3583572480908 11.8416427519092 55 102.7 98.9643198689533 3.73568013104673 56 95.1 101.152666905846 -6.05266690584585 57 117.5 113.935971327648 3.56402867235165 58 85.1 79.5655547432949 5.53444525670511 59 92.1 89.9181821153548 2.18181788464518 60 113.5 116.613811922124 -3.11381192212449

 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.0103166245804497 0.0206332491608995 0.98968337541955 20 0.0043575351734827 0.00871507034696539 0.995642464826517 21 0.00202856761597113 0.00405713523194227 0.997971432384029 22 0.000944964467037782 0.00188992893407556 0.999055035532962 23 0.000209186042294785 0.000418372084589569 0.999790813957705 24 0.000368108455547481 0.000736216911094961 0.999631891544453 25 0.000134538808541602 0.000269077617083203 0.999865461191458 26 0.0011168879489944 0.00223377589798881 0.998883112051006 27 0.0781291569964193 0.156258313992839 0.921870843003581 28 0.221697272512877 0.443394545025754 0.778302727487123 29 0.237274728586044 0.474549457172087 0.762725271413956 30 0.514763555358584 0.970472889282833 0.485236444641416 31 0.601748122144278 0.796503755711444 0.398251877855722 32 0.574134690284324 0.851730619431352 0.425865309715676 33 0.668014633116484 0.663970733767033 0.331985366883516 34 0.576248945884107 0.847502108231786 0.423751054115893 35 0.513339139610988 0.973321720778024 0.486660860389012 36 0.655682687559172 0.688634624881656 0.344317312440828 37 0.634153740103837 0.731692519792327 0.365846259896163 38 0.713955851244151 0.572088297511698 0.286044148755849 39 0.665646565351917 0.668706869296167 0.334353434648083 40 0.656596651836922 0.686806696326155 0.343403348163078 41 0.948782465762291 0.102435068475418 0.0512175342377092

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 & 0.0103166245804497 & 0.0206332491608995 & 0.98968337541955 \tabularnewline
20 & 0.0043575351734827 & 0.00871507034696539 & 0.995642464826517 \tabularnewline
21 & 0.00202856761597113 & 0.00405713523194227 & 0.997971432384029 \tabularnewline
22 & 0.000944964467037782 & 0.00188992893407556 & 0.999055035532962 \tabularnewline
23 & 0.000209186042294785 & 0.000418372084589569 & 0.999790813957705 \tabularnewline
24 & 0.000368108455547481 & 0.000736216911094961 & 0.999631891544453 \tabularnewline
25 & 0.000134538808541602 & 0.000269077617083203 & 0.999865461191458 \tabularnewline
26 & 0.0011168879489944 & 0.00223377589798881 & 0.998883112051006 \tabularnewline
27 & 0.0781291569964193 & 0.156258313992839 & 0.921870843003581 \tabularnewline
28 & 0.221697272512877 & 0.443394545025754 & 0.778302727487123 \tabularnewline
29 & 0.237274728586044 & 0.474549457172087 & 0.762725271413956 \tabularnewline
30 & 0.514763555358584 & 0.970472889282833 & 0.485236444641416 \tabularnewline
31 & 0.601748122144278 & 0.796503755711444 & 0.398251877855722 \tabularnewline
32 & 0.574134690284324 & 0.851730619431352 & 0.425865309715676 \tabularnewline
33 & 0.668014633116484 & 0.663970733767033 & 0.331985366883516 \tabularnewline
34 & 0.576248945884107 & 0.847502108231786 & 0.423751054115893 \tabularnewline
35 & 0.513339139610988 & 0.973321720778024 & 0.486660860389012 \tabularnewline
36 & 0.655682687559172 & 0.688634624881656 & 0.344317312440828 \tabularnewline
37 & 0.634153740103837 & 0.731692519792327 & 0.365846259896163 \tabularnewline
38 & 0.713955851244151 & 0.572088297511698 & 0.286044148755849 \tabularnewline
39 & 0.665646565351917 & 0.668706869296167 & 0.334353434648083 \tabularnewline
40 & 0.656596651836922 & 0.686806696326155 & 0.343403348163078 \tabularnewline
41 & 0.948782465762291 & 0.102435068475418 & 0.0512175342377092 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147440&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]19[/C][C]0.0103166245804497[/C][C]0.0206332491608995[/C][C]0.98968337541955[/C][/ROW]
[ROW][C]20[/C][C]0.0043575351734827[/C][C]0.00871507034696539[/C][C]0.995642464826517[/C][/ROW]
[ROW][C]21[/C][C]0.00202856761597113[/C][C]0.00405713523194227[/C][C]0.997971432384029[/C][/ROW]
[ROW][C]22[/C][C]0.000944964467037782[/C][C]0.00188992893407556[/C][C]0.999055035532962[/C][/ROW]
[ROW][C]23[/C][C]0.000209186042294785[/C][C]0.000418372084589569[/C][C]0.999790813957705[/C][/ROW]
[ROW][C]24[/C][C]0.000368108455547481[/C][C]0.000736216911094961[/C][C]0.999631891544453[/C][/ROW]
[ROW][C]25[/C][C]0.000134538808541602[/C][C]0.000269077617083203[/C][C]0.999865461191458[/C][/ROW]
[ROW][C]26[/C][C]0.0011168879489944[/C][C]0.00223377589798881[/C][C]0.998883112051006[/C][/ROW]
[ROW][C]27[/C][C]0.0781291569964193[/C][C]0.156258313992839[/C][C]0.921870843003581[/C][/ROW]
[ROW][C]28[/C][C]0.221697272512877[/C][C]0.443394545025754[/C][C]0.778302727487123[/C][/ROW]
[ROW][C]29[/C][C]0.237274728586044[/C][C]0.474549457172087[/C][C]0.762725271413956[/C][/ROW]
[ROW][C]30[/C][C]0.514763555358584[/C][C]0.970472889282833[/C][C]0.485236444641416[/C][/ROW]
[ROW][C]31[/C][C]0.601748122144278[/C][C]0.796503755711444[/C][C]0.398251877855722[/C][/ROW]
[ROW][C]32[/C][C]0.574134690284324[/C][C]0.851730619431352[/C][C]0.425865309715676[/C][/ROW]
[ROW][C]33[/C][C]0.668014633116484[/C][C]0.663970733767033[/C][C]0.331985366883516[/C][/ROW]
[ROW][C]34[/C][C]0.576248945884107[/C][C]0.847502108231786[/C][C]0.423751054115893[/C][/ROW]
[ROW][C]35[/C][C]0.513339139610988[/C][C]0.973321720778024[/C][C]0.486660860389012[/C][/ROW]
[ROW][C]36[/C][C]0.655682687559172[/C][C]0.688634624881656[/C][C]0.344317312440828[/C][/ROW]
[ROW][C]37[/C][C]0.634153740103837[/C][C]0.731692519792327[/C][C]0.365846259896163[/C][/ROW]
[ROW][C]38[/C][C]0.713955851244151[/C][C]0.572088297511698[/C][C]0.286044148755849[/C][/ROW]
[ROW][C]39[/C][C]0.665646565351917[/C][C]0.668706869296167[/C][C]0.334353434648083[/C][/ROW]
[ROW][C]40[/C][C]0.656596651836922[/C][C]0.686806696326155[/C][C]0.343403348163078[/C][/ROW]
[ROW][C]41[/C][C]0.948782465762291[/C][C]0.102435068475418[/C][C]0.0512175342377092[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147440&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147440&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 19 0.0103166245804497 0.0206332491608995 0.98968337541955 20 0.0043575351734827 0.00871507034696539 0.995642464826517 21 0.00202856761597113 0.00405713523194227 0.997971432384029 22 0.000944964467037782 0.00188992893407556 0.999055035532962 23 0.000209186042294785 0.000418372084589569 0.999790813957705 24 0.000368108455547481 0.000736216911094961 0.999631891544453 25 0.000134538808541602 0.000269077617083203 0.999865461191458 26 0.0011168879489944 0.00223377589798881 0.998883112051006 27 0.0781291569964193 0.156258313992839 0.921870843003581 28 0.221697272512877 0.443394545025754 0.778302727487123 29 0.237274728586044 0.474549457172087 0.762725271413956 30 0.514763555358584 0.970472889282833 0.485236444641416 31 0.601748122144278 0.796503755711444 0.398251877855722 32 0.574134690284324 0.851730619431352 0.425865309715676 33 0.668014633116484 0.663970733767033 0.331985366883516 34 0.576248945884107 0.847502108231786 0.423751054115893 35 0.513339139610988 0.973321720778024 0.486660860389012 36 0.655682687559172 0.688634624881656 0.344317312440828 37 0.634153740103837 0.731692519792327 0.365846259896163 38 0.713955851244151 0.572088297511698 0.286044148755849 39 0.665646565351917 0.668706869296167 0.334353434648083 40 0.656596651836922 0.686806696326155 0.343403348163078 41 0.948782465762291 0.102435068475418 0.0512175342377092

 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 7 0.304347826086957 NOK 5% type I error level 8 0.347826086956522 NOK 10% type I error level 8 0.347826086956522 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 & 7 & 0.304347826086957 & NOK \tabularnewline
5% type I error level & 8 & 0.347826086956522 & NOK \tabularnewline
10% type I error level & 8 & 0.347826086956522 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147440&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]7[/C][C]0.304347826086957[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]8[/C][C]0.347826086956522[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]8[/C][C]0.347826086956522[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147440&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147440&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 7 0.304347826086957 NOK 5% type I error level 8 0.347826086956522 NOK 10% type I error level 8 0.347826086956522 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')}