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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 07 Dec 2010 12:59:34 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/07/t1291726647vy7ri2gi81a90cb.htm/, Retrieved Fri, 29 Mar 2024 11:40:45 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=106255, Retrieved Fri, 29 Mar 2024 11:40:45 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact133
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 09:34:31] [21324e9cdf3569788a3d630236984d87]
-    D        [Multiple Regression] [] [2010-12-07 12:59:34] [1d208f56d63f78e3037c4c685f0bba30] [Current]
- R  D          [Multiple Regression] [] [2011-11-26 18:29:52] [74be16979710d4c4e7c6647856088456]
- R P             [Multiple Regression] [] [2011-11-27 16:56:17] [3931071255a6f7f4a767409781cc5f7d]
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Dataseries X:
112,3	0	117,2	96,8	80	126,1
117,3	0	112,3	117,2	96,8	80
111,1	1	117,3	112,3	117,2	96,8
102,2	1	111,1	117,3	112,3	117,2
104,3	1	102,2	111,1	117,3	112,3
122,9	1	104,3	102,2	111,1	117,3
107,6	1	122,9	104,3	102,2	111,1
121,3	1	107,6	122,9	104,3	102,2
131,5	1	121,3	107,6	122,9	104,3
89	1	131,5	121,3	107,6	122,9
104,4	1	89	131,5	121,3	107,6
128,9	1	104,4	89	131,5	121,3
135,9	1	128,9	104,4	89	131,5
133,3	1	135,9	128,9	104,4	89
121,3	1	133,3	135,9	128,9	104,4
120,5	0	121,3	133,3	135,9	128,9
120,4	0	120,5	121,3	133,3	135,9
137,9	0	120,4	120,5	121,3	133,3
126,1	0	137,9	120,4	120,5	121,3
133,2	0	126,1	137,9	120,4	120,5
151,1	0	133,2	126,1	137,9	120,4
105	0	151,1	133,2	126,1	137,9
119	0	105	151,1	133,2	126,1
140,4	0	119	105	151,1	133,2
156,6	0	140,4	119	105	151,1
137,1	0	156,6	140,4	119	105
122,7	0	137,1	156,6	140,4	119
125,8	0	122,7	137,1	156,6	140,4
139,3	0	125,8	122,7	137,1	156,6
134,9	0	139,3	125,8	122,7	137,1
149,2	0	134,9	139,3	125,8	122,7
132,3	0	149,2	134,9	139,3	125,8
149	0	132,3	149,2	134,9	139,3
117,2	0	149	132,3	149,2	134,9
119,6	0	117,2	149	132,3	149,2
152	0	119,6	117,2	149	132,3
149,4	0	152	119,6	117,2	149
127,3	0	149,4	152	119,6	117,2
114,1	0	127,3	149,4	152	119,6
102,1	0	114,1	127,3	149,4	152
107,7	0	102,1	114,1	127,3	149,4
104,4	0	107,7	102,1	114,1	127,3
102,1	0	104,4	107,7	102,1	114,1
96	1	102,1	104,4	107,7	102,1
109,3	0	96	102,1	104,4	107,7
90	1	109,3	96	102,1	104,4
83,9	1	90	109,3	96	102,1
112	1	83,9	90	109,3	96
114,3	1	112	83,9	90	109,3
103,6	1	114,3	112	83,9	90
91,7	1	103,6	114,3	112	83,9
80,8	1	91,7	103,6	114,3	112
87,2	1	80,8	91,7	103,6	114,3
109,2	1	87,2	80,8	91,7	103,6
102,7	1	109,2	87,2	80,8	91,7
95,1	1	102,7	109,2	87,2	80,8
117,5	1	95,1	102,7	109,2	87,2
85,1	1	117,5	95,1	102,7	109,2
92,1	1	85,1	117,5	95,1	102,7
113,5	1	92,1	85,1	117,5	95,1





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=106255&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=106255&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=106255&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.







Multiple Linear Regression - Estimated Regression Equation
X[t] = + 24.1698363156218 + 3.12409265557007Y[t] + 0.354106572914146`y(t)`[t] + 0.386685471568013`y(t-1)`[t] + 0.316297204680138`y(t-2)`[t] -0.0792357995923454`y(t-3)`[t] + 4.29883047671241M1[t] -22.2968495935551M2[t] -39.5691693349632M3[t] -35.9297143015368M4[t] -20.3378644943873M5[t] -6.9663341972657M6[t] -15.9323504617263M7[t] -22.9939682907714M8[t] -6.55872286116876M9[t] -44.2832772039972M10[t] -31.4045454857822M11[t] -0.0983757038209553t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
X[t] =  +  24.1698363156218 +  3.12409265557007Y[t] +  0.354106572914146`y(t)`[t] +  0.386685471568013`y(t-1)`[t] +  0.316297204680138`y(t-2)`[t] -0.0792357995923454`y(t-3)`[t] +  4.29883047671241M1[t] -22.2968495935551M2[t] -39.5691693349632M3[t] -35.9297143015368M4[t] -20.3378644943873M5[t] -6.9663341972657M6[t] -15.9323504617263M7[t] -22.9939682907714M8[t] -6.55872286116876M9[t] -44.2832772039972M10[t] -31.4045454857822M11[t] -0.0983757038209553t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=106255&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]X[t] =  +  24.1698363156218 +  3.12409265557007Y[t] +  0.354106572914146`y(t)`[t] +  0.386685471568013`y(t-1)`[t] +  0.316297204680138`y(t-2)`[t] -0.0792357995923454`y(t-3)`[t] +  4.29883047671241M1[t] -22.2968495935551M2[t] -39.5691693349632M3[t] -35.9297143015368M4[t] -20.3378644943873M5[t] -6.9663341972657M6[t] -15.9323504617263M7[t] -22.9939682907714M8[t] -6.55872286116876M9[t] -44.2832772039972M10[t] -31.4045454857822M11[t] -0.0983757038209553t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=106255&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=106255&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] = + 24.1698363156218 + 3.12409265557007Y[t] + 0.354106572914146`y(t)`[t] + 0.386685471568013`y(t-1)`[t] + 0.316297204680138`y(t-2)`[t] -0.0792357995923454`y(t-3)`[t] + 4.29883047671241M1[t] -22.2968495935551M2[t] -39.5691693349632M3[t] -35.9297143015368M4[t] -20.3378644943873M5[t] -6.9663341972657M6[t] -15.9323504617263M7[t] -22.9939682907714M8[t] -6.55872286116876M9[t] -44.2832772039972M10[t] -31.4045454857822M11[t] -0.0983757038209553t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)24.169836315621818.6389841.29670.2018010.100901
Y3.124092655570073.7730850.8280.4123540.206177
`y(t)`0.3541065729141460.1534972.30690.026060.01303
`y(t-1)`0.3866854715680130.1614542.3950.0211570.010578
`y(t-2)`0.3162972046801380.1562692.02410.0493560.024678
`y(t-3)`-0.07923579959234540.157901-0.50180.6184230.309212
M14.298830476712419.7994880.43870.6631420.331571
M2-22.296849593555110.749129-2.07430.0442180.022109
M3-39.56916933496327.999304-4.94661.3e-056e-06
M4-35.92971430153685.974976-6.013400
M5-20.33786449438736.115281-3.32570.0018380.000919
M6-6.96633419726576.522986-1.0680.2916360.145818
M7-15.93235046172637.900121-2.01670.0501480.025074
M8-22.99396829077147.837306-2.93390.0054040.002702
M9-6.558722861168766.469515-1.01380.3164880.158244
M10-44.28327720399727.364443-6.013100
M11-31.40454548578228.394784-3.7410.000550.000275
t-0.09837570382095530.065916-1.49240.1430570.071528

\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) & 24.1698363156218 & 18.638984 & 1.2967 & 0.201801 & 0.100901 \tabularnewline
Y & 3.12409265557007 & 3.773085 & 0.828 & 0.412354 & 0.206177 \tabularnewline
`y(t)` & 0.354106572914146 & 0.153497 & 2.3069 & 0.02606 & 0.01303 \tabularnewline
`y(t-1)` & 0.386685471568013 & 0.161454 & 2.395 & 0.021157 & 0.010578 \tabularnewline
`y(t-2)` & 0.316297204680138 & 0.156269 & 2.0241 & 0.049356 & 0.024678 \tabularnewline
`y(t-3)` & -0.0792357995923454 & 0.157901 & -0.5018 & 0.618423 & 0.309212 \tabularnewline
M1 & 4.29883047671241 & 9.799488 & 0.4387 & 0.663142 & 0.331571 \tabularnewline
M2 & -22.2968495935551 & 10.749129 & -2.0743 & 0.044218 & 0.022109 \tabularnewline
M3 & -39.5691693349632 & 7.999304 & -4.9466 & 1.3e-05 & 6e-06 \tabularnewline
M4 & -35.9297143015368 & 5.974976 & -6.0134 & 0 & 0 \tabularnewline
M5 & -20.3378644943873 & 6.115281 & -3.3257 & 0.001838 & 0.000919 \tabularnewline
M6 & -6.9663341972657 & 6.522986 & -1.068 & 0.291636 & 0.145818 \tabularnewline
M7 & -15.9323504617263 & 7.900121 & -2.0167 & 0.050148 & 0.025074 \tabularnewline
M8 & -22.9939682907714 & 7.837306 & -2.9339 & 0.005404 & 0.002702 \tabularnewline
M9 & -6.55872286116876 & 6.469515 & -1.0138 & 0.316488 & 0.158244 \tabularnewline
M10 & -44.2832772039972 & 7.364443 & -6.0131 & 0 & 0 \tabularnewline
M11 & -31.4045454857822 & 8.394784 & -3.741 & 0.00055 & 0.000275 \tabularnewline
t & -0.0983757038209553 & 0.065916 & -1.4924 & 0.143057 & 0.071528 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=106255&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]24.1698363156218[/C][C]18.638984[/C][C]1.2967[/C][C]0.201801[/C][C]0.100901[/C][/ROW]
[ROW][C]Y[/C][C]3.12409265557007[/C][C]3.773085[/C][C]0.828[/C][C]0.412354[/C][C]0.206177[/C][/ROW]
[ROW][C]`y(t)`[/C][C]0.354106572914146[/C][C]0.153497[/C][C]2.3069[/C][C]0.02606[/C][C]0.01303[/C][/ROW]
[ROW][C]`y(t-1)`[/C][C]0.386685471568013[/C][C]0.161454[/C][C]2.395[/C][C]0.021157[/C][C]0.010578[/C][/ROW]
[ROW][C]`y(t-2)`[/C][C]0.316297204680138[/C][C]0.156269[/C][C]2.0241[/C][C]0.049356[/C][C]0.024678[/C][/ROW]
[ROW][C]`y(t-3)`[/C][C]-0.0792357995923454[/C][C]0.157901[/C][C]-0.5018[/C][C]0.618423[/C][C]0.309212[/C][/ROW]
[ROW][C]M1[/C][C]4.29883047671241[/C][C]9.799488[/C][C]0.4387[/C][C]0.663142[/C][C]0.331571[/C][/ROW]
[ROW][C]M2[/C][C]-22.2968495935551[/C][C]10.749129[/C][C]-2.0743[/C][C]0.044218[/C][C]0.022109[/C][/ROW]
[ROW][C]M3[/C][C]-39.5691693349632[/C][C]7.999304[/C][C]-4.9466[/C][C]1.3e-05[/C][C]6e-06[/C][/ROW]
[ROW][C]M4[/C][C]-35.9297143015368[/C][C]5.974976[/C][C]-6.0134[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M5[/C][C]-20.3378644943873[/C][C]6.115281[/C][C]-3.3257[/C][C]0.001838[/C][C]0.000919[/C][/ROW]
[ROW][C]M6[/C][C]-6.9663341972657[/C][C]6.522986[/C][C]-1.068[/C][C]0.291636[/C][C]0.145818[/C][/ROW]
[ROW][C]M7[/C][C]-15.9323504617263[/C][C]7.900121[/C][C]-2.0167[/C][C]0.050148[/C][C]0.025074[/C][/ROW]
[ROW][C]M8[/C][C]-22.9939682907714[/C][C]7.837306[/C][C]-2.9339[/C][C]0.005404[/C][C]0.002702[/C][/ROW]
[ROW][C]M9[/C][C]-6.55872286116876[/C][C]6.469515[/C][C]-1.0138[/C][C]0.316488[/C][C]0.158244[/C][/ROW]
[ROW][C]M10[/C][C]-44.2832772039972[/C][C]7.364443[/C][C]-6.0131[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M11[/C][C]-31.4045454857822[/C][C]8.394784[/C][C]-3.741[/C][C]0.00055[/C][C]0.000275[/C][/ROW]
[ROW][C]t[/C][C]-0.0983757038209553[/C][C]0.065916[/C][C]-1.4924[/C][C]0.143057[/C][C]0.071528[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=106255&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=106255&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)24.169836315621818.6389841.29670.2018010.100901
Y3.124092655570073.7730850.8280.4123540.206177
`y(t)`0.3541065729141460.1534972.30690.026060.01303
`y(t-1)`0.3866854715680130.1614542.3950.0211570.010578
`y(t-2)`0.3162972046801380.1562692.02410.0493560.024678
`y(t-3)`-0.07923579959234540.157901-0.50180.6184230.309212
M14.298830476712419.7994880.43870.6631420.331571
M2-22.296849593555110.749129-2.07430.0442180.022109
M3-39.56916933496327.999304-4.94661.3e-056e-06
M4-35.92971430153685.974976-6.013400
M5-20.33786449438736.115281-3.32570.0018380.000919
M6-6.96633419726576.522986-1.0680.2916360.145818
M7-15.93235046172637.900121-2.01670.0501480.025074
M8-22.99396829077147.837306-2.93390.0054040.002702
M9-6.558722861168766.469515-1.01380.3164880.158244
M10-44.28327720399727.364443-6.013100
M11-31.40454548578228.394784-3.7410.000550.000275
t-0.09837570382095530.065916-1.49240.1430570.071528







Multiple Linear Regression - Regression Statistics
Multiple R0.938958363418636
R-squared0.881642808233803
Adjusted R-squared0.833736325852247
F-TEST (value)18.4034135758888
F-TEST (DF numerator)17
F-TEST (DF denominator)42
p-value3.19744231092045e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation7.73467319800471
Sum Squared Residuals2512.65711815716

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.938958363418636 \tabularnewline
R-squared & 0.881642808233803 \tabularnewline
Adjusted R-squared & 0.833736325852247 \tabularnewline
F-TEST (value) & 18.4034135758888 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 42 \tabularnewline
p-value & 3.19744231092045e-14 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 7.73467319800471 \tabularnewline
Sum Squared Residuals & 2512.65711815716 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=106255&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.938958363418636[/C][/ROW]
[ROW][C]R-squared[/C][C]0.881642808233803[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.833736325852247[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]18.4034135758888[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]42[/C][/ROW]
[ROW][C]p-value[/C][C]3.19744231092045e-14[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]7.73467319800471[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2512.65711815716[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=106255&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=106255&T=3

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.938958363418636
R-squared0.881642808233803
Adjusted R-squared0.833736325852247
F-TEST (value)18.4034135758888
F-TEST (DF numerator)17
F-TEST (DF denominator)42
p-value3.19744231092045e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation7.73467319800471
Sum Squared Residuals2512.65711815716







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1112.3122.614877127651-10.3148771276511
2117.3111.0406461661046.25935383389579
3111.1101.7911189726569.3088810273439
4102.2101.9038982934170.296101706582654
5104.3113.818115415492-9.5181154154915
6122.9122.0361714479780.863828552022036
7107.6118.046418062012-10.4464180620117
8121.3114.0303664809247.26963351907558
9131.5135.018941368546-3.51894136854592
108999.7923562220788-10.7923562220788
11104.4107.012954135496-2.61295413549615
12128.9129.478933632017-0.578933632017009
13135.9134.0591193487051.84088065129534
14133.3127.556102073185.74389792681967
15121.3118.5005780402922.79942195970812
16120.5113.9357069560296.56429304397087
17120.4123.128646812895-2.72864681289544
18137.9132.4674889944295.43251100557129
19126.1130.259085336352-4.15908533635198
20133.2125.7193889147457.48061108525491
21151.1145.5506514055765.54934859442373
22105111.692762354131-6.69276235413127
23119118.2511678866690.74883211333072
24140.4141.787775236812-1.38777523681202
25156.6142.98008532356113.619914676439
26137.1138.378556348966-1.27855634896627
27122.7126.026546357175-3.3265463571753
28125.8120.3564929457835.44350705421719
29139.3123.92801118990715.3719888100929
30134.9140.170747824066-5.27074782406636
31149.2136.89005764976912.309942350231
32132.3137.116753319122-4.81675331912151
33149150.537433210987-1.53743321098744
34117.2116.9647860076370.235213992362869
35119.6118.4637056852821.13629431471772
36152144.9443815776447.05561842235639
37149.4150.164445482696-0.764445482696067
38127.3138.357133616103-11.0571336161034
39114.1122.21316419601-8.11316419600972
40102.1109.144675202535-7.04467520253496
41107.7108.500467061705-0.800467061705028
42104.4116.692380873722-12.2923808737218
43102.1105.875221954062-3.77522195406175
4496102.470907844206-6.47090784420568
45109.3111.146756981873-1.84675698187297
469078.332750201877311.6672497981227
4783.987.6745955213964-3.77459552139643
48112114.047776807078-2.04777680707792
49114.3118.681472717387-4.38147271738714
50103.6103.2675617956460.332438204354196
5191.792.368592433867-0.668592433867005
5280.886.0592266022358-5.25922660223576
5387.289.5247595200009-2.3247595200009
54109.297.933210859805211.2667891401948
55102.796.62921699780566.07078300219443
5695.198.5625834410033-3.4625834410033
57117.5116.1462170330171.3537829669826
5885.179.51734521427555.58265478572454
5992.187.59757677115584.50242322884415
60113.5116.541132746449-3.04113274644943

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 112.3 & 122.614877127651 & -10.3148771276511 \tabularnewline
2 & 117.3 & 111.040646166104 & 6.25935383389579 \tabularnewline
3 & 111.1 & 101.791118972656 & 9.3088810273439 \tabularnewline
4 & 102.2 & 101.903898293417 & 0.296101706582654 \tabularnewline
5 & 104.3 & 113.818115415492 & -9.5181154154915 \tabularnewline
6 & 122.9 & 122.036171447978 & 0.863828552022036 \tabularnewline
7 & 107.6 & 118.046418062012 & -10.4464180620117 \tabularnewline
8 & 121.3 & 114.030366480924 & 7.26963351907558 \tabularnewline
9 & 131.5 & 135.018941368546 & -3.51894136854592 \tabularnewline
10 & 89 & 99.7923562220788 & -10.7923562220788 \tabularnewline
11 & 104.4 & 107.012954135496 & -2.61295413549615 \tabularnewline
12 & 128.9 & 129.478933632017 & -0.578933632017009 \tabularnewline
13 & 135.9 & 134.059119348705 & 1.84088065129534 \tabularnewline
14 & 133.3 & 127.55610207318 & 5.74389792681967 \tabularnewline
15 & 121.3 & 118.500578040292 & 2.79942195970812 \tabularnewline
16 & 120.5 & 113.935706956029 & 6.56429304397087 \tabularnewline
17 & 120.4 & 123.128646812895 & -2.72864681289544 \tabularnewline
18 & 137.9 & 132.467488994429 & 5.43251100557129 \tabularnewline
19 & 126.1 & 130.259085336352 & -4.15908533635198 \tabularnewline
20 & 133.2 & 125.719388914745 & 7.48061108525491 \tabularnewline
21 & 151.1 & 145.550651405576 & 5.54934859442373 \tabularnewline
22 & 105 & 111.692762354131 & -6.69276235413127 \tabularnewline
23 & 119 & 118.251167886669 & 0.74883211333072 \tabularnewline
24 & 140.4 & 141.787775236812 & -1.38777523681202 \tabularnewline
25 & 156.6 & 142.980085323561 & 13.619914676439 \tabularnewline
26 & 137.1 & 138.378556348966 & -1.27855634896627 \tabularnewline
27 & 122.7 & 126.026546357175 & -3.3265463571753 \tabularnewline
28 & 125.8 & 120.356492945783 & 5.44350705421719 \tabularnewline
29 & 139.3 & 123.928011189907 & 15.3719888100929 \tabularnewline
30 & 134.9 & 140.170747824066 & -5.27074782406636 \tabularnewline
31 & 149.2 & 136.890057649769 & 12.309942350231 \tabularnewline
32 & 132.3 & 137.116753319122 & -4.81675331912151 \tabularnewline
33 & 149 & 150.537433210987 & -1.53743321098744 \tabularnewline
34 & 117.2 & 116.964786007637 & 0.235213992362869 \tabularnewline
35 & 119.6 & 118.463705685282 & 1.13629431471772 \tabularnewline
36 & 152 & 144.944381577644 & 7.05561842235639 \tabularnewline
37 & 149.4 & 150.164445482696 & -0.764445482696067 \tabularnewline
38 & 127.3 & 138.357133616103 & -11.0571336161034 \tabularnewline
39 & 114.1 & 122.21316419601 & -8.11316419600972 \tabularnewline
40 & 102.1 & 109.144675202535 & -7.04467520253496 \tabularnewline
41 & 107.7 & 108.500467061705 & -0.800467061705028 \tabularnewline
42 & 104.4 & 116.692380873722 & -12.2923808737218 \tabularnewline
43 & 102.1 & 105.875221954062 & -3.77522195406175 \tabularnewline
44 & 96 & 102.470907844206 & -6.47090784420568 \tabularnewline
45 & 109.3 & 111.146756981873 & -1.84675698187297 \tabularnewline
46 & 90 & 78.3327502018773 & 11.6672497981227 \tabularnewline
47 & 83.9 & 87.6745955213964 & -3.77459552139643 \tabularnewline
48 & 112 & 114.047776807078 & -2.04777680707792 \tabularnewline
49 & 114.3 & 118.681472717387 & -4.38147271738714 \tabularnewline
50 & 103.6 & 103.267561795646 & 0.332438204354196 \tabularnewline
51 & 91.7 & 92.368592433867 & -0.668592433867005 \tabularnewline
52 & 80.8 & 86.0592266022358 & -5.25922660223576 \tabularnewline
53 & 87.2 & 89.5247595200009 & -2.3247595200009 \tabularnewline
54 & 109.2 & 97.9332108598052 & 11.2667891401948 \tabularnewline
55 & 102.7 & 96.6292169978056 & 6.07078300219443 \tabularnewline
56 & 95.1 & 98.5625834410033 & -3.4625834410033 \tabularnewline
57 & 117.5 & 116.146217033017 & 1.3537829669826 \tabularnewline
58 & 85.1 & 79.5173452142755 & 5.58265478572454 \tabularnewline
59 & 92.1 & 87.5975767711558 & 4.50242322884415 \tabularnewline
60 & 113.5 & 116.541132746449 & -3.04113274644943 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=106255&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]122.614877127651[/C][C]-10.3148771276511[/C][/ROW]
[ROW][C]2[/C][C]117.3[/C][C]111.040646166104[/C][C]6.25935383389579[/C][/ROW]
[ROW][C]3[/C][C]111.1[/C][C]101.791118972656[/C][C]9.3088810273439[/C][/ROW]
[ROW][C]4[/C][C]102.2[/C][C]101.903898293417[/C][C]0.296101706582654[/C][/ROW]
[ROW][C]5[/C][C]104.3[/C][C]113.818115415492[/C][C]-9.5181154154915[/C][/ROW]
[ROW][C]6[/C][C]122.9[/C][C]122.036171447978[/C][C]0.863828552022036[/C][/ROW]
[ROW][C]7[/C][C]107.6[/C][C]118.046418062012[/C][C]-10.4464180620117[/C][/ROW]
[ROW][C]8[/C][C]121.3[/C][C]114.030366480924[/C][C]7.26963351907558[/C][/ROW]
[ROW][C]9[/C][C]131.5[/C][C]135.018941368546[/C][C]-3.51894136854592[/C][/ROW]
[ROW][C]10[/C][C]89[/C][C]99.7923562220788[/C][C]-10.7923562220788[/C][/ROW]
[ROW][C]11[/C][C]104.4[/C][C]107.012954135496[/C][C]-2.61295413549615[/C][/ROW]
[ROW][C]12[/C][C]128.9[/C][C]129.478933632017[/C][C]-0.578933632017009[/C][/ROW]
[ROW][C]13[/C][C]135.9[/C][C]134.059119348705[/C][C]1.84088065129534[/C][/ROW]
[ROW][C]14[/C][C]133.3[/C][C]127.55610207318[/C][C]5.74389792681967[/C][/ROW]
[ROW][C]15[/C][C]121.3[/C][C]118.500578040292[/C][C]2.79942195970812[/C][/ROW]
[ROW][C]16[/C][C]120.5[/C][C]113.935706956029[/C][C]6.56429304397087[/C][/ROW]
[ROW][C]17[/C][C]120.4[/C][C]123.128646812895[/C][C]-2.72864681289544[/C][/ROW]
[ROW][C]18[/C][C]137.9[/C][C]132.467488994429[/C][C]5.43251100557129[/C][/ROW]
[ROW][C]19[/C][C]126.1[/C][C]130.259085336352[/C][C]-4.15908533635198[/C][/ROW]
[ROW][C]20[/C][C]133.2[/C][C]125.719388914745[/C][C]7.48061108525491[/C][/ROW]
[ROW][C]21[/C][C]151.1[/C][C]145.550651405576[/C][C]5.54934859442373[/C][/ROW]
[ROW][C]22[/C][C]105[/C][C]111.692762354131[/C][C]-6.69276235413127[/C][/ROW]
[ROW][C]23[/C][C]119[/C][C]118.251167886669[/C][C]0.74883211333072[/C][/ROW]
[ROW][C]24[/C][C]140.4[/C][C]141.787775236812[/C][C]-1.38777523681202[/C][/ROW]
[ROW][C]25[/C][C]156.6[/C][C]142.980085323561[/C][C]13.619914676439[/C][/ROW]
[ROW][C]26[/C][C]137.1[/C][C]138.378556348966[/C][C]-1.27855634896627[/C][/ROW]
[ROW][C]27[/C][C]122.7[/C][C]126.026546357175[/C][C]-3.3265463571753[/C][/ROW]
[ROW][C]28[/C][C]125.8[/C][C]120.356492945783[/C][C]5.44350705421719[/C][/ROW]
[ROW][C]29[/C][C]139.3[/C][C]123.928011189907[/C][C]15.3719888100929[/C][/ROW]
[ROW][C]30[/C][C]134.9[/C][C]140.170747824066[/C][C]-5.27074782406636[/C][/ROW]
[ROW][C]31[/C][C]149.2[/C][C]136.890057649769[/C][C]12.309942350231[/C][/ROW]
[ROW][C]32[/C][C]132.3[/C][C]137.116753319122[/C][C]-4.81675331912151[/C][/ROW]
[ROW][C]33[/C][C]149[/C][C]150.537433210987[/C][C]-1.53743321098744[/C][/ROW]
[ROW][C]34[/C][C]117.2[/C][C]116.964786007637[/C][C]0.235213992362869[/C][/ROW]
[ROW][C]35[/C][C]119.6[/C][C]118.463705685282[/C][C]1.13629431471772[/C][/ROW]
[ROW][C]36[/C][C]152[/C][C]144.944381577644[/C][C]7.05561842235639[/C][/ROW]
[ROW][C]37[/C][C]149.4[/C][C]150.164445482696[/C][C]-0.764445482696067[/C][/ROW]
[ROW][C]38[/C][C]127.3[/C][C]138.357133616103[/C][C]-11.0571336161034[/C][/ROW]
[ROW][C]39[/C][C]114.1[/C][C]122.21316419601[/C][C]-8.11316419600972[/C][/ROW]
[ROW][C]40[/C][C]102.1[/C][C]109.144675202535[/C][C]-7.04467520253496[/C][/ROW]
[ROW][C]41[/C][C]107.7[/C][C]108.500467061705[/C][C]-0.800467061705028[/C][/ROW]
[ROW][C]42[/C][C]104.4[/C][C]116.692380873722[/C][C]-12.2923808737218[/C][/ROW]
[ROW][C]43[/C][C]102.1[/C][C]105.875221954062[/C][C]-3.77522195406175[/C][/ROW]
[ROW][C]44[/C][C]96[/C][C]102.470907844206[/C][C]-6.47090784420568[/C][/ROW]
[ROW][C]45[/C][C]109.3[/C][C]111.146756981873[/C][C]-1.84675698187297[/C][/ROW]
[ROW][C]46[/C][C]90[/C][C]78.3327502018773[/C][C]11.6672497981227[/C][/ROW]
[ROW][C]47[/C][C]83.9[/C][C]87.6745955213964[/C][C]-3.77459552139643[/C][/ROW]
[ROW][C]48[/C][C]112[/C][C]114.047776807078[/C][C]-2.04777680707792[/C][/ROW]
[ROW][C]49[/C][C]114.3[/C][C]118.681472717387[/C][C]-4.38147271738714[/C][/ROW]
[ROW][C]50[/C][C]103.6[/C][C]103.267561795646[/C][C]0.332438204354196[/C][/ROW]
[ROW][C]51[/C][C]91.7[/C][C]92.368592433867[/C][C]-0.668592433867005[/C][/ROW]
[ROW][C]52[/C][C]80.8[/C][C]86.0592266022358[/C][C]-5.25922660223576[/C][/ROW]
[ROW][C]53[/C][C]87.2[/C][C]89.5247595200009[/C][C]-2.3247595200009[/C][/ROW]
[ROW][C]54[/C][C]109.2[/C][C]97.9332108598052[/C][C]11.2667891401948[/C][/ROW]
[ROW][C]55[/C][C]102.7[/C][C]96.6292169978056[/C][C]6.07078300219443[/C][/ROW]
[ROW][C]56[/C][C]95.1[/C][C]98.5625834410033[/C][C]-3.4625834410033[/C][/ROW]
[ROW][C]57[/C][C]117.5[/C][C]116.146217033017[/C][C]1.3537829669826[/C][/ROW]
[ROW][C]58[/C][C]85.1[/C][C]79.5173452142755[/C][C]5.58265478572454[/C][/ROW]
[ROW][C]59[/C][C]92.1[/C][C]87.5975767711558[/C][C]4.50242322884415[/C][/ROW]
[ROW][C]60[/C][C]113.5[/C][C]116.541132746449[/C][C]-3.04113274644943[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=106255&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=106255&T=4

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1112.3122.614877127651-10.3148771276511
2117.3111.0406461661046.25935383389579
3111.1101.7911189726569.3088810273439
4102.2101.9038982934170.296101706582654
5104.3113.818115415492-9.5181154154915
6122.9122.0361714479780.863828552022036
7107.6118.046418062012-10.4464180620117
8121.3114.0303664809247.26963351907558
9131.5135.018941368546-3.51894136854592
108999.7923562220788-10.7923562220788
11104.4107.012954135496-2.61295413549615
12128.9129.478933632017-0.578933632017009
13135.9134.0591193487051.84088065129534
14133.3127.556102073185.74389792681967
15121.3118.5005780402922.79942195970812
16120.5113.9357069560296.56429304397087
17120.4123.128646812895-2.72864681289544
18137.9132.4674889944295.43251100557129
19126.1130.259085336352-4.15908533635198
20133.2125.7193889147457.48061108525491
21151.1145.5506514055765.54934859442373
22105111.692762354131-6.69276235413127
23119118.2511678866690.74883211333072
24140.4141.787775236812-1.38777523681202
25156.6142.98008532356113.619914676439
26137.1138.378556348966-1.27855634896627
27122.7126.026546357175-3.3265463571753
28125.8120.3564929457835.44350705421719
29139.3123.92801118990715.3719888100929
30134.9140.170747824066-5.27074782406636
31149.2136.89005764976912.309942350231
32132.3137.116753319122-4.81675331912151
33149150.537433210987-1.53743321098744
34117.2116.9647860076370.235213992362869
35119.6118.4637056852821.13629431471772
36152144.9443815776447.05561842235639
37149.4150.164445482696-0.764445482696067
38127.3138.357133616103-11.0571336161034
39114.1122.21316419601-8.11316419600972
40102.1109.144675202535-7.04467520253496
41107.7108.500467061705-0.800467061705028
42104.4116.692380873722-12.2923808737218
43102.1105.875221954062-3.77522195406175
4496102.470907844206-6.47090784420568
45109.3111.146756981873-1.84675698187297
469078.332750201877311.6672497981227
4783.987.6745955213964-3.77459552139643
48112114.047776807078-2.04777680707792
49114.3118.681472717387-4.38147271738714
50103.6103.2675617956460.332438204354196
5191.792.368592433867-0.668592433867005
5280.886.0592266022358-5.25922660223576
5387.289.5247595200009-2.3247595200009
54109.297.933210859805211.2667891401948
55102.796.62921699780566.07078300219443
5695.198.5625834410033-3.4625834410033
57117.5116.1462170330171.3537829669826
5885.179.51734521427555.58265478572454
5992.187.59757677115584.50242322884415
60113.5116.541132746449-3.04113274644943







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.02599734881168940.05199469762337890.97400265118831
220.008086277984762380.01617255596952480.991913722015238
230.001803603887778550.00360720777555710.998196396112221
240.002275122198873860.004550244397747730.997724877801126
250.0008654850374799670.001730970074959930.99913451496252
260.00487259117129010.00974518234258020.99512740882871
270.1140087418931560.2280174837863110.885991258106844
280.3078054434329450.615610886865890.692194556567055
290.3323833128300790.6647666256601570.667616687169921
300.2911401810720040.5822803621440070.708859818927996
310.5939275480010240.8121449039979510.406072451998976
320.5175188770473290.9649622459053420.482481122952671
330.7043062810377710.5913874379244580.295693718962229
340.5997720583350180.8004558833299650.400227941664982
350.5120295903143820.9759408193712370.487970409685618
360.7449612387327650.5100775225344710.255038761267235
370.6389562866082740.7220874267834520.361043713391726
380.6344583207135010.7310833585729970.365541679286499
390.5276491356894530.9447017286210930.472350864310547

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.0259973488116894 & 0.0519946976233789 & 0.97400265118831 \tabularnewline
22 & 0.00808627798476238 & 0.0161725559695248 & 0.991913722015238 \tabularnewline
23 & 0.00180360388777855 & 0.0036072077755571 & 0.998196396112221 \tabularnewline
24 & 0.00227512219887386 & 0.00455024439774773 & 0.997724877801126 \tabularnewline
25 & 0.000865485037479967 & 0.00173097007495993 & 0.99913451496252 \tabularnewline
26 & 0.0048725911712901 & 0.0097451823425802 & 0.99512740882871 \tabularnewline
27 & 0.114008741893156 & 0.228017483786311 & 0.885991258106844 \tabularnewline
28 & 0.307805443432945 & 0.61561088686589 & 0.692194556567055 \tabularnewline
29 & 0.332383312830079 & 0.664766625660157 & 0.667616687169921 \tabularnewline
30 & 0.291140181072004 & 0.582280362144007 & 0.708859818927996 \tabularnewline
31 & 0.593927548001024 & 0.812144903997951 & 0.406072451998976 \tabularnewline
32 & 0.517518877047329 & 0.964962245905342 & 0.482481122952671 \tabularnewline
33 & 0.704306281037771 & 0.591387437924458 & 0.295693718962229 \tabularnewline
34 & 0.599772058335018 & 0.800455883329965 & 0.400227941664982 \tabularnewline
35 & 0.512029590314382 & 0.975940819371237 & 0.487970409685618 \tabularnewline
36 & 0.744961238732765 & 0.510077522534471 & 0.255038761267235 \tabularnewline
37 & 0.638956286608274 & 0.722087426783452 & 0.361043713391726 \tabularnewline
38 & 0.634458320713501 & 0.731083358572997 & 0.365541679286499 \tabularnewline
39 & 0.527649135689453 & 0.944701728621093 & 0.472350864310547 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=106255&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]21[/C][C]0.0259973488116894[/C][C]0.0519946976233789[/C][C]0.97400265118831[/C][/ROW]
[ROW][C]22[/C][C]0.00808627798476238[/C][C]0.0161725559695248[/C][C]0.991913722015238[/C][/ROW]
[ROW][C]23[/C][C]0.00180360388777855[/C][C]0.0036072077755571[/C][C]0.998196396112221[/C][/ROW]
[ROW][C]24[/C][C]0.00227512219887386[/C][C]0.00455024439774773[/C][C]0.997724877801126[/C][/ROW]
[ROW][C]25[/C][C]0.000865485037479967[/C][C]0.00173097007495993[/C][C]0.99913451496252[/C][/ROW]
[ROW][C]26[/C][C]0.0048725911712901[/C][C]0.0097451823425802[/C][C]0.99512740882871[/C][/ROW]
[ROW][C]27[/C][C]0.114008741893156[/C][C]0.228017483786311[/C][C]0.885991258106844[/C][/ROW]
[ROW][C]28[/C][C]0.307805443432945[/C][C]0.61561088686589[/C][C]0.692194556567055[/C][/ROW]
[ROW][C]29[/C][C]0.332383312830079[/C][C]0.664766625660157[/C][C]0.667616687169921[/C][/ROW]
[ROW][C]30[/C][C]0.291140181072004[/C][C]0.582280362144007[/C][C]0.708859818927996[/C][/ROW]
[ROW][C]31[/C][C]0.593927548001024[/C][C]0.812144903997951[/C][C]0.406072451998976[/C][/ROW]
[ROW][C]32[/C][C]0.517518877047329[/C][C]0.964962245905342[/C][C]0.482481122952671[/C][/ROW]
[ROW][C]33[/C][C]0.704306281037771[/C][C]0.591387437924458[/C][C]0.295693718962229[/C][/ROW]
[ROW][C]34[/C][C]0.599772058335018[/C][C]0.800455883329965[/C][C]0.400227941664982[/C][/ROW]
[ROW][C]35[/C][C]0.512029590314382[/C][C]0.975940819371237[/C][C]0.487970409685618[/C][/ROW]
[ROW][C]36[/C][C]0.744961238732765[/C][C]0.510077522534471[/C][C]0.255038761267235[/C][/ROW]
[ROW][C]37[/C][C]0.638956286608274[/C][C]0.722087426783452[/C][C]0.361043713391726[/C][/ROW]
[ROW][C]38[/C][C]0.634458320713501[/C][C]0.731083358572997[/C][C]0.365541679286499[/C][/ROW]
[ROW][C]39[/C][C]0.527649135689453[/C][C]0.944701728621093[/C][C]0.472350864310547[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=106255&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=106255&T=5

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.02599734881168940.05199469762337890.97400265118831
220.008086277984762380.01617255596952480.991913722015238
230.001803603887778550.00360720777555710.998196396112221
240.002275122198873860.004550244397747730.997724877801126
250.0008654850374799670.001730970074959930.99913451496252
260.00487259117129010.00974518234258020.99512740882871
270.1140087418931560.2280174837863110.885991258106844
280.3078054434329450.615610886865890.692194556567055
290.3323833128300790.6647666256601570.667616687169921
300.2911401810720040.5822803621440070.708859818927996
310.5939275480010240.8121449039979510.406072451998976
320.5175188770473290.9649622459053420.482481122952671
330.7043062810377710.5913874379244580.295693718962229
340.5997720583350180.8004558833299650.400227941664982
350.5120295903143820.9759408193712370.487970409685618
360.7449612387327650.5100775225344710.255038761267235
370.6389562866082740.7220874267834520.361043713391726
380.6344583207135010.7310833585729970.365541679286499
390.5276491356894530.9447017286210930.472350864310547







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level40.210526315789474NOK
5% type I error level50.263157894736842NOK
10% type I error level60.315789473684211NOK

\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 & 4 & 0.210526315789474 & NOK \tabularnewline
5% type I error level & 5 & 0.263157894736842 & NOK \tabularnewline
10% type I error level & 6 & 0.315789473684211 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=106255&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]4[/C][C]0.210526315789474[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]5[/C][C]0.263157894736842[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]6[/C][C]0.315789473684211[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=106255&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=106255&T=6

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level40.210526315789474NOK
5% type I error level50.263157894736842NOK
10% type I error level60.315789473684211NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}