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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 04:43:51 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t1258717478i102gjsx5cbwr5c.htm/, Retrieved Fri, 29 Mar 2024 09:09:56 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58045, Retrieved Fri, 29 Mar 2024 09:09:56 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact130
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [] [2009-11-19 17:49:26] [badc6a9acdc45286bea7f74742e15a21]
-   PD        [Multiple Regression] [] [2009-11-20 11:43:51] [0545e25c765ce26b196961216dc11e13] [Current]
-   PD          [Multiple Regression] [] [2009-11-20 12:06:30] [badc6a9acdc45286bea7f74742e15a21]
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Dataseries X:
1.4	2
1.2	2
1	2
1.7	2
2.4	2
2	2
2.1	2
2	2
1.8	2
2.7	2
2.3	2
1.9	2
2	2
2.3	2
2.8	2
2.4	2
2.3	2
2.7	2
2.7	2
2.9	2
3	2
2.2	2
2.3	2
2.8	2.21
2.8	2.25
2.8	2.25
2.2	2.45
2.6	2.5
2.8	2.5
2.5	2.64
2.4	2.75
2.3	2.93
1.9	3
1.7	3.17
2	3.25
2.1	3.39
1.7	3.5
1.8	3.5
1.8	3.65
1.8	3.75
1.3	3.75
1.3	3.9
1.3	4
1.2	4
1.4	4
2.2	4
2.9	4
3.1	4
3.5	4
3.6	4
4.4	4
4.1	4
5.1	4
5.8	4
5.9	4.18
5.4	4.25
5.5	4.25
4.8	3.97
3.2	3.42
2.7	2.75
2.1	2.31
1.9	2
0.6	1.66
0.7	1.31
-0.2	1.09
-1	1
-1.7	1
-0.7	1
-1	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58045&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58045&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58045&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = -0.113771342246555 + 0.91769036315211X[t] -0.0925798631239247M1[t] -0.0284991943610653M2[t] -0.163362011632986M3[t] -0.0494389995279161M4[t] + 0.0508763137876616M5[t] -0.0463800316507418M6[t] -0.206029905255629M7[t] -0.177600337053634M8[t] -0.271640057957075M9[t] + 0.055004922621967M10[t] -0.0387321832417344M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  -0.113771342246555 +  0.91769036315211X[t] -0.0925798631239247M1[t] -0.0284991943610653M2[t] -0.163362011632986M3[t] -0.0494389995279161M4[t] +  0.0508763137876616M5[t] -0.0463800316507418M6[t] -0.206029905255629M7[t] -0.177600337053634M8[t] -0.271640057957075M9[t] +  0.055004922621967M10[t] -0.0387321832417344M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58045&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  -0.113771342246555 +  0.91769036315211X[t] -0.0925798631239247M1[t] -0.0284991943610653M2[t] -0.163362011632986M3[t] -0.0494389995279161M4[t] +  0.0508763137876616M5[t] -0.0463800316507418M6[t] -0.206029905255629M7[t] -0.177600337053634M8[t] -0.271640057957075M9[t] +  0.055004922621967M10[t] -0.0387321832417344M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58045&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58045&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
Y[t] = -0.113771342246555 + 0.91769036315211X[t] -0.0925798631239247M1[t] -0.0284991943610653M2[t] -0.163362011632986M3[t] -0.0494389995279161M4[t] + 0.0508763137876616M5[t] -0.0463800316507418M6[t] -0.206029905255629M7[t] -0.177600337053634M8[t] -0.271640057957075M9[t] + 0.055004922621967M10[t] -0.0387321832417344M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.1137713422465550.716703-0.15870.8744430.437221
X0.917690363152110.155565.899300
M1-0.09257986312392470.759731-0.12190.9034470.451724
M2-0.02849919436106530.760091-0.03750.9702240.485112
M3-0.1633620116329860.760078-0.21490.8306040.415302
M4-0.04943899952791610.760354-0.0650.9483890.474194
M50.05087631378766160.7606980.06690.9469150.473457
M6-0.04638003165074180.760384-0.0610.951580.47579
M7-0.2060299052556290.759872-0.27110.7872820.393641
M8-0.1776003370536340.759614-0.23380.815990.407995
M9-0.2716400579570750.759552-0.35760.7219630.360982
M100.0550049226219670.7932720.06930.9449670.472483
M11-0.03873218324173440.792953-0.04880.9612160.480608

\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) & -0.113771342246555 & 0.716703 & -0.1587 & 0.874443 & 0.437221 \tabularnewline
X & 0.91769036315211 & 0.15556 & 5.8993 & 0 & 0 \tabularnewline
M1 & -0.0925798631239247 & 0.759731 & -0.1219 & 0.903447 & 0.451724 \tabularnewline
M2 & -0.0284991943610653 & 0.760091 & -0.0375 & 0.970224 & 0.485112 \tabularnewline
M3 & -0.163362011632986 & 0.760078 & -0.2149 & 0.830604 & 0.415302 \tabularnewline
M4 & -0.0494389995279161 & 0.760354 & -0.065 & 0.948389 & 0.474194 \tabularnewline
M5 & 0.0508763137876616 & 0.760698 & 0.0669 & 0.946915 & 0.473457 \tabularnewline
M6 & -0.0463800316507418 & 0.760384 & -0.061 & 0.95158 & 0.47579 \tabularnewline
M7 & -0.206029905255629 & 0.759872 & -0.2711 & 0.787282 & 0.393641 \tabularnewline
M8 & -0.177600337053634 & 0.759614 & -0.2338 & 0.81599 & 0.407995 \tabularnewline
M9 & -0.271640057957075 & 0.759552 & -0.3576 & 0.721963 & 0.360982 \tabularnewline
M10 & 0.055004922621967 & 0.793272 & 0.0693 & 0.944967 & 0.472483 \tabularnewline
M11 & -0.0387321832417344 & 0.792953 & -0.0488 & 0.961216 & 0.480608 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58045&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]-0.113771342246555[/C][C]0.716703[/C][C]-0.1587[/C][C]0.874443[/C][C]0.437221[/C][/ROW]
[ROW][C]X[/C][C]0.91769036315211[/C][C]0.15556[/C][C]5.8993[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-0.0925798631239247[/C][C]0.759731[/C][C]-0.1219[/C][C]0.903447[/C][C]0.451724[/C][/ROW]
[ROW][C]M2[/C][C]-0.0284991943610653[/C][C]0.760091[/C][C]-0.0375[/C][C]0.970224[/C][C]0.485112[/C][/ROW]
[ROW][C]M3[/C][C]-0.163362011632986[/C][C]0.760078[/C][C]-0.2149[/C][C]0.830604[/C][C]0.415302[/C][/ROW]
[ROW][C]M4[/C][C]-0.0494389995279161[/C][C]0.760354[/C][C]-0.065[/C][C]0.948389[/C][C]0.474194[/C][/ROW]
[ROW][C]M5[/C][C]0.0508763137876616[/C][C]0.760698[/C][C]0.0669[/C][C]0.946915[/C][C]0.473457[/C][/ROW]
[ROW][C]M6[/C][C]-0.0463800316507418[/C][C]0.760384[/C][C]-0.061[/C][C]0.95158[/C][C]0.47579[/C][/ROW]
[ROW][C]M7[/C][C]-0.206029905255629[/C][C]0.759872[/C][C]-0.2711[/C][C]0.787282[/C][C]0.393641[/C][/ROW]
[ROW][C]M8[/C][C]-0.177600337053634[/C][C]0.759614[/C][C]-0.2338[/C][C]0.81599[/C][C]0.407995[/C][/ROW]
[ROW][C]M9[/C][C]-0.271640057957075[/C][C]0.759552[/C][C]-0.3576[/C][C]0.721963[/C][C]0.360982[/C][/ROW]
[ROW][C]M10[/C][C]0.055004922621967[/C][C]0.793272[/C][C]0.0693[/C][C]0.944967[/C][C]0.472483[/C][/ROW]
[ROW][C]M11[/C][C]-0.0387321832417344[/C][C]0.792953[/C][C]-0.0488[/C][C]0.961216[/C][C]0.480608[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58045&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58045&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)-0.1137713422465550.716703-0.15870.8744430.437221
X0.917690363152110.155565.899300
M1-0.09257986312392470.759731-0.12190.9034470.451724
M2-0.02849919436106530.760091-0.03750.9702240.485112
M3-0.1633620116329860.760078-0.21490.8306040.415302
M4-0.04943899952791610.760354-0.0650.9483890.474194
M50.05087631378766160.7606980.06690.9469150.473457
M6-0.04638003165074180.760384-0.0610.951580.47579
M7-0.2060299052556290.759872-0.27110.7872820.393641
M8-0.1776003370536340.759614-0.23380.815990.407995
M9-0.2716400579570750.759552-0.35760.7219630.360982
M100.0550049226219670.7932720.06930.9449670.472483
M11-0.03873218324173440.792953-0.04880.9612160.480608







Multiple Linear Regression - Regression Statistics
Multiple R0.626562034371423
R-squared0.392579982915657
Adjusted R-squared0.262418550683297
F-TEST (value)3.01610066962722
F-TEST (DF numerator)12
F-TEST (DF denominator)56
p-value0.00255174287534687
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.25367055569551
Sum Squared Residuals88.0146322842021

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.626562034371423 \tabularnewline
R-squared & 0.392579982915657 \tabularnewline
Adjusted R-squared & 0.262418550683297 \tabularnewline
F-TEST (value) & 3.01610066962722 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 56 \tabularnewline
p-value & 0.00255174287534687 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.25367055569551 \tabularnewline
Sum Squared Residuals & 88.0146322842021 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58045&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.626562034371423[/C][/ROW]
[ROW][C]R-squared[/C][C]0.392579982915657[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.262418550683297[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.01610066962722[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]56[/C][/ROW]
[ROW][C]p-value[/C][C]0.00255174287534687[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.25367055569551[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]88.0146322842021[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58045&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58045&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 R0.626562034371423
R-squared0.392579982915657
Adjusted R-squared0.262418550683297
F-TEST (value)3.01610066962722
F-TEST (DF numerator)12
F-TEST (DF denominator)56
p-value0.00255174287534687
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.25367055569551
Sum Squared Residuals88.0146322842021







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11.41.62902952093374-0.229029520933737
21.21.69311018969660-0.493110189696596
311.55824737242468-0.558247372424678
41.71.672170384529750.0278296154702518
52.41.772485697845330.627514302154674
621.675229352406920.324770647593079
72.11.515579478802030.584420521197966
821.544009047004030.455990952995969
91.81.449969326100590.350030673899411
102.71.776614306679630.92338569332037
112.31.682877200815930.617122799184071
121.91.721609384057660.178390615942336
1321.629029520933740.370970479066261
142.31.69311018969660.606889810303401
152.81.558247372424681.24175262757532
162.41.672170384529750.727829615470252
172.31.772485697845330.527514302154674
182.71.675229352406921.02477064759308
192.71.515579478802031.18442052119797
202.91.544009047004031.35599095299597
2131.449969326100591.55003067389941
222.21.776614306679630.423385693320369
232.31.682877200815930.61712279918407
242.81.914324360319610.885675639680393
252.81.858452111721770.941547888278233
262.81.922532780484630.877467219515374
272.21.971208035843130.228791964156873
282.62.13101556610580.468984433894197
292.82.231330879421380.56866912057862
302.52.262551184824270.237448815175728
312.42.203847251166120.196152748833883
322.32.39746108473549-0.097461084735493
331.92.3676596892527-0.467659689252699
341.72.8503120315676-1.1503120315676
3522.82999015475607-0.829990154756067
362.12.99719898883910-0.897198988839096
371.73.00556506566190-1.30556506566190
381.83.06964573442476-1.26964573442476
391.83.07243647162566-1.27243647162566
401.83.27812852004594-1.47812852004594
411.33.37844383336152-2.07844383336152
421.33.41884104239593-2.11884104239593
431.33.35096020510625-2.05096020510625
441.23.37938977330825-2.17938977330825
451.43.28535005240481-1.88535005240481
462.23.61199503298385-1.41199503298385
472.93.51825792712015-0.618257927120149
483.13.55699011036188-0.456990110361884
493.53.464410247237960.035589752762041
503.63.528490916000820.0715090839991817
514.43.39362809872891.00637190127110
524.13.507551110833970.592448889166032
535.13.607866424149551.49213357585045
545.83.510610078711142.28938992128886
555.93.516144470473632.38385552952637
565.43.608812364096281.79118763590372
575.53.514772643192841.98522735680716
584.83.584464322089291.21553567791071
593.22.985997516491930.214002483508075
602.72.409877156421750.290122843578254
612.11.913513533510890.186486466489107
621.91.69311018969660.206889810303402
630.61.24623264895296-0.64623264895296
640.71.03896403395479-0.338964033954792
65-0.20.937387467376906-1.13738746737691
66-10.757538989254811-1.75753898925481
67-1.70.597889115649925-2.29788911564992
68-0.70.62631868385192-1.32631868385192
69-10.532278962948479-1.53227896294848

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.4 & 1.62902952093374 & -0.229029520933737 \tabularnewline
2 & 1.2 & 1.69311018969660 & -0.493110189696596 \tabularnewline
3 & 1 & 1.55824737242468 & -0.558247372424678 \tabularnewline
4 & 1.7 & 1.67217038452975 & 0.0278296154702518 \tabularnewline
5 & 2.4 & 1.77248569784533 & 0.627514302154674 \tabularnewline
6 & 2 & 1.67522935240692 & 0.324770647593079 \tabularnewline
7 & 2.1 & 1.51557947880203 & 0.584420521197966 \tabularnewline
8 & 2 & 1.54400904700403 & 0.455990952995969 \tabularnewline
9 & 1.8 & 1.44996932610059 & 0.350030673899411 \tabularnewline
10 & 2.7 & 1.77661430667963 & 0.92338569332037 \tabularnewline
11 & 2.3 & 1.68287720081593 & 0.617122799184071 \tabularnewline
12 & 1.9 & 1.72160938405766 & 0.178390615942336 \tabularnewline
13 & 2 & 1.62902952093374 & 0.370970479066261 \tabularnewline
14 & 2.3 & 1.6931101896966 & 0.606889810303401 \tabularnewline
15 & 2.8 & 1.55824737242468 & 1.24175262757532 \tabularnewline
16 & 2.4 & 1.67217038452975 & 0.727829615470252 \tabularnewline
17 & 2.3 & 1.77248569784533 & 0.527514302154674 \tabularnewline
18 & 2.7 & 1.67522935240692 & 1.02477064759308 \tabularnewline
19 & 2.7 & 1.51557947880203 & 1.18442052119797 \tabularnewline
20 & 2.9 & 1.54400904700403 & 1.35599095299597 \tabularnewline
21 & 3 & 1.44996932610059 & 1.55003067389941 \tabularnewline
22 & 2.2 & 1.77661430667963 & 0.423385693320369 \tabularnewline
23 & 2.3 & 1.68287720081593 & 0.61712279918407 \tabularnewline
24 & 2.8 & 1.91432436031961 & 0.885675639680393 \tabularnewline
25 & 2.8 & 1.85845211172177 & 0.941547888278233 \tabularnewline
26 & 2.8 & 1.92253278048463 & 0.877467219515374 \tabularnewline
27 & 2.2 & 1.97120803584313 & 0.228791964156873 \tabularnewline
28 & 2.6 & 2.1310155661058 & 0.468984433894197 \tabularnewline
29 & 2.8 & 2.23133087942138 & 0.56866912057862 \tabularnewline
30 & 2.5 & 2.26255118482427 & 0.237448815175728 \tabularnewline
31 & 2.4 & 2.20384725116612 & 0.196152748833883 \tabularnewline
32 & 2.3 & 2.39746108473549 & -0.097461084735493 \tabularnewline
33 & 1.9 & 2.3676596892527 & -0.467659689252699 \tabularnewline
34 & 1.7 & 2.8503120315676 & -1.1503120315676 \tabularnewline
35 & 2 & 2.82999015475607 & -0.829990154756067 \tabularnewline
36 & 2.1 & 2.99719898883910 & -0.897198988839096 \tabularnewline
37 & 1.7 & 3.00556506566190 & -1.30556506566190 \tabularnewline
38 & 1.8 & 3.06964573442476 & -1.26964573442476 \tabularnewline
39 & 1.8 & 3.07243647162566 & -1.27243647162566 \tabularnewline
40 & 1.8 & 3.27812852004594 & -1.47812852004594 \tabularnewline
41 & 1.3 & 3.37844383336152 & -2.07844383336152 \tabularnewline
42 & 1.3 & 3.41884104239593 & -2.11884104239593 \tabularnewline
43 & 1.3 & 3.35096020510625 & -2.05096020510625 \tabularnewline
44 & 1.2 & 3.37938977330825 & -2.17938977330825 \tabularnewline
45 & 1.4 & 3.28535005240481 & -1.88535005240481 \tabularnewline
46 & 2.2 & 3.61199503298385 & -1.41199503298385 \tabularnewline
47 & 2.9 & 3.51825792712015 & -0.618257927120149 \tabularnewline
48 & 3.1 & 3.55699011036188 & -0.456990110361884 \tabularnewline
49 & 3.5 & 3.46441024723796 & 0.035589752762041 \tabularnewline
50 & 3.6 & 3.52849091600082 & 0.0715090839991817 \tabularnewline
51 & 4.4 & 3.3936280987289 & 1.00637190127110 \tabularnewline
52 & 4.1 & 3.50755111083397 & 0.592448889166032 \tabularnewline
53 & 5.1 & 3.60786642414955 & 1.49213357585045 \tabularnewline
54 & 5.8 & 3.51061007871114 & 2.28938992128886 \tabularnewline
55 & 5.9 & 3.51614447047363 & 2.38385552952637 \tabularnewline
56 & 5.4 & 3.60881236409628 & 1.79118763590372 \tabularnewline
57 & 5.5 & 3.51477264319284 & 1.98522735680716 \tabularnewline
58 & 4.8 & 3.58446432208929 & 1.21553567791071 \tabularnewline
59 & 3.2 & 2.98599751649193 & 0.214002483508075 \tabularnewline
60 & 2.7 & 2.40987715642175 & 0.290122843578254 \tabularnewline
61 & 2.1 & 1.91351353351089 & 0.186486466489107 \tabularnewline
62 & 1.9 & 1.6931101896966 & 0.206889810303402 \tabularnewline
63 & 0.6 & 1.24623264895296 & -0.64623264895296 \tabularnewline
64 & 0.7 & 1.03896403395479 & -0.338964033954792 \tabularnewline
65 & -0.2 & 0.937387467376906 & -1.13738746737691 \tabularnewline
66 & -1 & 0.757538989254811 & -1.75753898925481 \tabularnewline
67 & -1.7 & 0.597889115649925 & -2.29788911564992 \tabularnewline
68 & -0.7 & 0.62631868385192 & -1.32631868385192 \tabularnewline
69 & -1 & 0.532278962948479 & -1.53227896294848 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58045&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]1.4[/C][C]1.62902952093374[/C][C]-0.229029520933737[/C][/ROW]
[ROW][C]2[/C][C]1.2[/C][C]1.69311018969660[/C][C]-0.493110189696596[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]1.55824737242468[/C][C]-0.558247372424678[/C][/ROW]
[ROW][C]4[/C][C]1.7[/C][C]1.67217038452975[/C][C]0.0278296154702518[/C][/ROW]
[ROW][C]5[/C][C]2.4[/C][C]1.77248569784533[/C][C]0.627514302154674[/C][/ROW]
[ROW][C]6[/C][C]2[/C][C]1.67522935240692[/C][C]0.324770647593079[/C][/ROW]
[ROW][C]7[/C][C]2.1[/C][C]1.51557947880203[/C][C]0.584420521197966[/C][/ROW]
[ROW][C]8[/C][C]2[/C][C]1.54400904700403[/C][C]0.455990952995969[/C][/ROW]
[ROW][C]9[/C][C]1.8[/C][C]1.44996932610059[/C][C]0.350030673899411[/C][/ROW]
[ROW][C]10[/C][C]2.7[/C][C]1.77661430667963[/C][C]0.92338569332037[/C][/ROW]
[ROW][C]11[/C][C]2.3[/C][C]1.68287720081593[/C][C]0.617122799184071[/C][/ROW]
[ROW][C]12[/C][C]1.9[/C][C]1.72160938405766[/C][C]0.178390615942336[/C][/ROW]
[ROW][C]13[/C][C]2[/C][C]1.62902952093374[/C][C]0.370970479066261[/C][/ROW]
[ROW][C]14[/C][C]2.3[/C][C]1.6931101896966[/C][C]0.606889810303401[/C][/ROW]
[ROW][C]15[/C][C]2.8[/C][C]1.55824737242468[/C][C]1.24175262757532[/C][/ROW]
[ROW][C]16[/C][C]2.4[/C][C]1.67217038452975[/C][C]0.727829615470252[/C][/ROW]
[ROW][C]17[/C][C]2.3[/C][C]1.77248569784533[/C][C]0.527514302154674[/C][/ROW]
[ROW][C]18[/C][C]2.7[/C][C]1.67522935240692[/C][C]1.02477064759308[/C][/ROW]
[ROW][C]19[/C][C]2.7[/C][C]1.51557947880203[/C][C]1.18442052119797[/C][/ROW]
[ROW][C]20[/C][C]2.9[/C][C]1.54400904700403[/C][C]1.35599095299597[/C][/ROW]
[ROW][C]21[/C][C]3[/C][C]1.44996932610059[/C][C]1.55003067389941[/C][/ROW]
[ROW][C]22[/C][C]2.2[/C][C]1.77661430667963[/C][C]0.423385693320369[/C][/ROW]
[ROW][C]23[/C][C]2.3[/C][C]1.68287720081593[/C][C]0.61712279918407[/C][/ROW]
[ROW][C]24[/C][C]2.8[/C][C]1.91432436031961[/C][C]0.885675639680393[/C][/ROW]
[ROW][C]25[/C][C]2.8[/C][C]1.85845211172177[/C][C]0.941547888278233[/C][/ROW]
[ROW][C]26[/C][C]2.8[/C][C]1.92253278048463[/C][C]0.877467219515374[/C][/ROW]
[ROW][C]27[/C][C]2.2[/C][C]1.97120803584313[/C][C]0.228791964156873[/C][/ROW]
[ROW][C]28[/C][C]2.6[/C][C]2.1310155661058[/C][C]0.468984433894197[/C][/ROW]
[ROW][C]29[/C][C]2.8[/C][C]2.23133087942138[/C][C]0.56866912057862[/C][/ROW]
[ROW][C]30[/C][C]2.5[/C][C]2.26255118482427[/C][C]0.237448815175728[/C][/ROW]
[ROW][C]31[/C][C]2.4[/C][C]2.20384725116612[/C][C]0.196152748833883[/C][/ROW]
[ROW][C]32[/C][C]2.3[/C][C]2.39746108473549[/C][C]-0.097461084735493[/C][/ROW]
[ROW][C]33[/C][C]1.9[/C][C]2.3676596892527[/C][C]-0.467659689252699[/C][/ROW]
[ROW][C]34[/C][C]1.7[/C][C]2.8503120315676[/C][C]-1.1503120315676[/C][/ROW]
[ROW][C]35[/C][C]2[/C][C]2.82999015475607[/C][C]-0.829990154756067[/C][/ROW]
[ROW][C]36[/C][C]2.1[/C][C]2.99719898883910[/C][C]-0.897198988839096[/C][/ROW]
[ROW][C]37[/C][C]1.7[/C][C]3.00556506566190[/C][C]-1.30556506566190[/C][/ROW]
[ROW][C]38[/C][C]1.8[/C][C]3.06964573442476[/C][C]-1.26964573442476[/C][/ROW]
[ROW][C]39[/C][C]1.8[/C][C]3.07243647162566[/C][C]-1.27243647162566[/C][/ROW]
[ROW][C]40[/C][C]1.8[/C][C]3.27812852004594[/C][C]-1.47812852004594[/C][/ROW]
[ROW][C]41[/C][C]1.3[/C][C]3.37844383336152[/C][C]-2.07844383336152[/C][/ROW]
[ROW][C]42[/C][C]1.3[/C][C]3.41884104239593[/C][C]-2.11884104239593[/C][/ROW]
[ROW][C]43[/C][C]1.3[/C][C]3.35096020510625[/C][C]-2.05096020510625[/C][/ROW]
[ROW][C]44[/C][C]1.2[/C][C]3.37938977330825[/C][C]-2.17938977330825[/C][/ROW]
[ROW][C]45[/C][C]1.4[/C][C]3.28535005240481[/C][C]-1.88535005240481[/C][/ROW]
[ROW][C]46[/C][C]2.2[/C][C]3.61199503298385[/C][C]-1.41199503298385[/C][/ROW]
[ROW][C]47[/C][C]2.9[/C][C]3.51825792712015[/C][C]-0.618257927120149[/C][/ROW]
[ROW][C]48[/C][C]3.1[/C][C]3.55699011036188[/C][C]-0.456990110361884[/C][/ROW]
[ROW][C]49[/C][C]3.5[/C][C]3.46441024723796[/C][C]0.035589752762041[/C][/ROW]
[ROW][C]50[/C][C]3.6[/C][C]3.52849091600082[/C][C]0.0715090839991817[/C][/ROW]
[ROW][C]51[/C][C]4.4[/C][C]3.3936280987289[/C][C]1.00637190127110[/C][/ROW]
[ROW][C]52[/C][C]4.1[/C][C]3.50755111083397[/C][C]0.592448889166032[/C][/ROW]
[ROW][C]53[/C][C]5.1[/C][C]3.60786642414955[/C][C]1.49213357585045[/C][/ROW]
[ROW][C]54[/C][C]5.8[/C][C]3.51061007871114[/C][C]2.28938992128886[/C][/ROW]
[ROW][C]55[/C][C]5.9[/C][C]3.51614447047363[/C][C]2.38385552952637[/C][/ROW]
[ROW][C]56[/C][C]5.4[/C][C]3.60881236409628[/C][C]1.79118763590372[/C][/ROW]
[ROW][C]57[/C][C]5.5[/C][C]3.51477264319284[/C][C]1.98522735680716[/C][/ROW]
[ROW][C]58[/C][C]4.8[/C][C]3.58446432208929[/C][C]1.21553567791071[/C][/ROW]
[ROW][C]59[/C][C]3.2[/C][C]2.98599751649193[/C][C]0.214002483508075[/C][/ROW]
[ROW][C]60[/C][C]2.7[/C][C]2.40987715642175[/C][C]0.290122843578254[/C][/ROW]
[ROW][C]61[/C][C]2.1[/C][C]1.91351353351089[/C][C]0.186486466489107[/C][/ROW]
[ROW][C]62[/C][C]1.9[/C][C]1.6931101896966[/C][C]0.206889810303402[/C][/ROW]
[ROW][C]63[/C][C]0.6[/C][C]1.24623264895296[/C][C]-0.64623264895296[/C][/ROW]
[ROW][C]64[/C][C]0.7[/C][C]1.03896403395479[/C][C]-0.338964033954792[/C][/ROW]
[ROW][C]65[/C][C]-0.2[/C][C]0.937387467376906[/C][C]-1.13738746737691[/C][/ROW]
[ROW][C]66[/C][C]-1[/C][C]0.757538989254811[/C][C]-1.75753898925481[/C][/ROW]
[ROW][C]67[/C][C]-1.7[/C][C]0.597889115649925[/C][C]-2.29788911564992[/C][/ROW]
[ROW][C]68[/C][C]-0.7[/C][C]0.62631868385192[/C][C]-1.32631868385192[/C][/ROW]
[ROW][C]69[/C][C]-1[/C][C]0.532278962948479[/C][C]-1.53227896294848[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58045&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58045&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
11.41.62902952093374-0.229029520933737
21.21.69311018969660-0.493110189696596
311.55824737242468-0.558247372424678
41.71.672170384529750.0278296154702518
52.41.772485697845330.627514302154674
621.675229352406920.324770647593079
72.11.515579478802030.584420521197966
821.544009047004030.455990952995969
91.81.449969326100590.350030673899411
102.71.776614306679630.92338569332037
112.31.682877200815930.617122799184071
121.91.721609384057660.178390615942336
1321.629029520933740.370970479066261
142.31.69311018969660.606889810303401
152.81.558247372424681.24175262757532
162.41.672170384529750.727829615470252
172.31.772485697845330.527514302154674
182.71.675229352406921.02477064759308
192.71.515579478802031.18442052119797
202.91.544009047004031.35599095299597
2131.449969326100591.55003067389941
222.21.776614306679630.423385693320369
232.31.682877200815930.61712279918407
242.81.914324360319610.885675639680393
252.81.858452111721770.941547888278233
262.81.922532780484630.877467219515374
272.21.971208035843130.228791964156873
282.62.13101556610580.468984433894197
292.82.231330879421380.56866912057862
302.52.262551184824270.237448815175728
312.42.203847251166120.196152748833883
322.32.39746108473549-0.097461084735493
331.92.3676596892527-0.467659689252699
341.72.8503120315676-1.1503120315676
3522.82999015475607-0.829990154756067
362.12.99719898883910-0.897198988839096
371.73.00556506566190-1.30556506566190
381.83.06964573442476-1.26964573442476
391.83.07243647162566-1.27243647162566
401.83.27812852004594-1.47812852004594
411.33.37844383336152-2.07844383336152
421.33.41884104239593-2.11884104239593
431.33.35096020510625-2.05096020510625
441.23.37938977330825-2.17938977330825
451.43.28535005240481-1.88535005240481
462.23.61199503298385-1.41199503298385
472.93.51825792712015-0.618257927120149
483.13.55699011036188-0.456990110361884
493.53.464410247237960.035589752762041
503.63.528490916000820.0715090839991817
514.43.39362809872891.00637190127110
524.13.507551110833970.592448889166032
535.13.607866424149551.49213357585045
545.83.510610078711142.28938992128886
555.93.516144470473632.38385552952637
565.43.608812364096281.79118763590372
575.53.514772643192841.98522735680716
584.83.584464322089291.21553567791071
593.22.985997516491930.214002483508075
602.72.409877156421750.290122843578254
612.11.913513533510890.186486466489107
621.91.69311018969660.206889810303402
630.61.24623264895296-0.64623264895296
640.71.03896403395479-0.338964033954792
65-0.20.937387467376906-1.13738746737691
66-10.757538989254811-1.75753898925481
67-1.70.597889115649925-2.29788911564992
68-0.70.62631868385192-1.32631868385192
69-10.532278962948479-1.53227896294848







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.2716170705965620.5432341411931240.728382929403438
170.1385417686600630.2770835373201270.861458231339937
180.0789277840534550.157855568106910.921072215946545
190.0435419700634680.0870839401269360.956458029936532
200.02961192281306470.05922384562612950.970388077186935
210.02744160828111000.05488321656222010.97255839171889
220.01463192903526570.02926385807053140.985368070964734
230.006989828690178010.01397965738035600.993010171309822
240.003305795466800390.006611590933600770.9966942045332
250.001541738122440750.003083476244881510.99845826187756
260.0006918481911994950.001383696382398990.9993081518088
270.0006825030463087330.001365006092617470.999317496953691
280.000333232141373090.000666464282746180.999666767858627
290.0001605964631323660.0003211929262647330.999839403536868
309.2026202691436e-050.0001840524053828720.999907973797309
315.37366783881139e-050.0001074733567762280.999946263321612
322.97596600300527e-055.95193200601055e-050.99997024033997
331.81012770883963e-053.62025541767927e-050.999981898722912
341.15872292575225e-052.31744585150451e-050.999988412770742
354.21209320490344e-068.42418640980689e-060.999995787906795
361.50500642268526e-063.01001284537052e-060.999998494993577
376.54242088049186e-071.30848417609837e-060.999999345757912
382.80614837912025e-075.6122967582405e-070.999999719385162
391.29979638438219e-072.59959276876439e-070.999999870020362
407.60870347538039e-081.52174069507608e-070.999999923912965
411.96674797758102e-073.93349595516204e-070.999999803325202
426.42757174953938e-071.28551434990788e-060.999999357242825
432.16452600486395e-064.32905200972791e-060.999997835473995
442.65473640077253e-055.30947280154506e-050.999973452635992
450.0004498909005878290.0008997818011756580.999550109099412
460.002542547239005350.005085094478010690.997457452760995
470.00500379421497480.01000758842994960.994996205785025
480.01642911072075450.03285822144150890.983570889279246
490.06808937859132160.1361787571826430.931910621408678
500.2763853335261880.5527706670523760.723614666473812
510.360077850480390.720155700960780.63992214951961
520.7618741453229490.4762517093541020.238125854677051
530.77567391010570.4486521797885990.224326089894299

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.271617070596562 & 0.543234141193124 & 0.728382929403438 \tabularnewline
17 & 0.138541768660063 & 0.277083537320127 & 0.861458231339937 \tabularnewline
18 & 0.078927784053455 & 0.15785556810691 & 0.921072215946545 \tabularnewline
19 & 0.043541970063468 & 0.087083940126936 & 0.956458029936532 \tabularnewline
20 & 0.0296119228130647 & 0.0592238456261295 & 0.970388077186935 \tabularnewline
21 & 0.0274416082811100 & 0.0548832165622201 & 0.97255839171889 \tabularnewline
22 & 0.0146319290352657 & 0.0292638580705314 & 0.985368070964734 \tabularnewline
23 & 0.00698982869017801 & 0.0139796573803560 & 0.993010171309822 \tabularnewline
24 & 0.00330579546680039 & 0.00661159093360077 & 0.9966942045332 \tabularnewline
25 & 0.00154173812244075 & 0.00308347624488151 & 0.99845826187756 \tabularnewline
26 & 0.000691848191199495 & 0.00138369638239899 & 0.9993081518088 \tabularnewline
27 & 0.000682503046308733 & 0.00136500609261747 & 0.999317496953691 \tabularnewline
28 & 0.00033323214137309 & 0.00066646428274618 & 0.999666767858627 \tabularnewline
29 & 0.000160596463132366 & 0.000321192926264733 & 0.999839403536868 \tabularnewline
30 & 9.2026202691436e-05 & 0.000184052405382872 & 0.999907973797309 \tabularnewline
31 & 5.37366783881139e-05 & 0.000107473356776228 & 0.999946263321612 \tabularnewline
32 & 2.97596600300527e-05 & 5.95193200601055e-05 & 0.99997024033997 \tabularnewline
33 & 1.81012770883963e-05 & 3.62025541767927e-05 & 0.999981898722912 \tabularnewline
34 & 1.15872292575225e-05 & 2.31744585150451e-05 & 0.999988412770742 \tabularnewline
35 & 4.21209320490344e-06 & 8.42418640980689e-06 & 0.999995787906795 \tabularnewline
36 & 1.50500642268526e-06 & 3.01001284537052e-06 & 0.999998494993577 \tabularnewline
37 & 6.54242088049186e-07 & 1.30848417609837e-06 & 0.999999345757912 \tabularnewline
38 & 2.80614837912025e-07 & 5.6122967582405e-07 & 0.999999719385162 \tabularnewline
39 & 1.29979638438219e-07 & 2.59959276876439e-07 & 0.999999870020362 \tabularnewline
40 & 7.60870347538039e-08 & 1.52174069507608e-07 & 0.999999923912965 \tabularnewline
41 & 1.96674797758102e-07 & 3.93349595516204e-07 & 0.999999803325202 \tabularnewline
42 & 6.42757174953938e-07 & 1.28551434990788e-06 & 0.999999357242825 \tabularnewline
43 & 2.16452600486395e-06 & 4.32905200972791e-06 & 0.999997835473995 \tabularnewline
44 & 2.65473640077253e-05 & 5.30947280154506e-05 & 0.999973452635992 \tabularnewline
45 & 0.000449890900587829 & 0.000899781801175658 & 0.999550109099412 \tabularnewline
46 & 0.00254254723900535 & 0.00508509447801069 & 0.997457452760995 \tabularnewline
47 & 0.0050037942149748 & 0.0100075884299496 & 0.994996205785025 \tabularnewline
48 & 0.0164291107207545 & 0.0328582214415089 & 0.983570889279246 \tabularnewline
49 & 0.0680893785913216 & 0.136178757182643 & 0.931910621408678 \tabularnewline
50 & 0.276385333526188 & 0.552770667052376 & 0.723614666473812 \tabularnewline
51 & 0.36007785048039 & 0.72015570096078 & 0.63992214951961 \tabularnewline
52 & 0.761874145322949 & 0.476251709354102 & 0.238125854677051 \tabularnewline
53 & 0.7756739101057 & 0.448652179788599 & 0.224326089894299 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58045&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]16[/C][C]0.271617070596562[/C][C]0.543234141193124[/C][C]0.728382929403438[/C][/ROW]
[ROW][C]17[/C][C]0.138541768660063[/C][C]0.277083537320127[/C][C]0.861458231339937[/C][/ROW]
[ROW][C]18[/C][C]0.078927784053455[/C][C]0.15785556810691[/C][C]0.921072215946545[/C][/ROW]
[ROW][C]19[/C][C]0.043541970063468[/C][C]0.087083940126936[/C][C]0.956458029936532[/C][/ROW]
[ROW][C]20[/C][C]0.0296119228130647[/C][C]0.0592238456261295[/C][C]0.970388077186935[/C][/ROW]
[ROW][C]21[/C][C]0.0274416082811100[/C][C]0.0548832165622201[/C][C]0.97255839171889[/C][/ROW]
[ROW][C]22[/C][C]0.0146319290352657[/C][C]0.0292638580705314[/C][C]0.985368070964734[/C][/ROW]
[ROW][C]23[/C][C]0.00698982869017801[/C][C]0.0139796573803560[/C][C]0.993010171309822[/C][/ROW]
[ROW][C]24[/C][C]0.00330579546680039[/C][C]0.00661159093360077[/C][C]0.9966942045332[/C][/ROW]
[ROW][C]25[/C][C]0.00154173812244075[/C][C]0.00308347624488151[/C][C]0.99845826187756[/C][/ROW]
[ROW][C]26[/C][C]0.000691848191199495[/C][C]0.00138369638239899[/C][C]0.9993081518088[/C][/ROW]
[ROW][C]27[/C][C]0.000682503046308733[/C][C]0.00136500609261747[/C][C]0.999317496953691[/C][/ROW]
[ROW][C]28[/C][C]0.00033323214137309[/C][C]0.00066646428274618[/C][C]0.999666767858627[/C][/ROW]
[ROW][C]29[/C][C]0.000160596463132366[/C][C]0.000321192926264733[/C][C]0.999839403536868[/C][/ROW]
[ROW][C]30[/C][C]9.2026202691436e-05[/C][C]0.000184052405382872[/C][C]0.999907973797309[/C][/ROW]
[ROW][C]31[/C][C]5.37366783881139e-05[/C][C]0.000107473356776228[/C][C]0.999946263321612[/C][/ROW]
[ROW][C]32[/C][C]2.97596600300527e-05[/C][C]5.95193200601055e-05[/C][C]0.99997024033997[/C][/ROW]
[ROW][C]33[/C][C]1.81012770883963e-05[/C][C]3.62025541767927e-05[/C][C]0.999981898722912[/C][/ROW]
[ROW][C]34[/C][C]1.15872292575225e-05[/C][C]2.31744585150451e-05[/C][C]0.999988412770742[/C][/ROW]
[ROW][C]35[/C][C]4.21209320490344e-06[/C][C]8.42418640980689e-06[/C][C]0.999995787906795[/C][/ROW]
[ROW][C]36[/C][C]1.50500642268526e-06[/C][C]3.01001284537052e-06[/C][C]0.999998494993577[/C][/ROW]
[ROW][C]37[/C][C]6.54242088049186e-07[/C][C]1.30848417609837e-06[/C][C]0.999999345757912[/C][/ROW]
[ROW][C]38[/C][C]2.80614837912025e-07[/C][C]5.6122967582405e-07[/C][C]0.999999719385162[/C][/ROW]
[ROW][C]39[/C][C]1.29979638438219e-07[/C][C]2.59959276876439e-07[/C][C]0.999999870020362[/C][/ROW]
[ROW][C]40[/C][C]7.60870347538039e-08[/C][C]1.52174069507608e-07[/C][C]0.999999923912965[/C][/ROW]
[ROW][C]41[/C][C]1.96674797758102e-07[/C][C]3.93349595516204e-07[/C][C]0.999999803325202[/C][/ROW]
[ROW][C]42[/C][C]6.42757174953938e-07[/C][C]1.28551434990788e-06[/C][C]0.999999357242825[/C][/ROW]
[ROW][C]43[/C][C]2.16452600486395e-06[/C][C]4.32905200972791e-06[/C][C]0.999997835473995[/C][/ROW]
[ROW][C]44[/C][C]2.65473640077253e-05[/C][C]5.30947280154506e-05[/C][C]0.999973452635992[/C][/ROW]
[ROW][C]45[/C][C]0.000449890900587829[/C][C]0.000899781801175658[/C][C]0.999550109099412[/C][/ROW]
[ROW][C]46[/C][C]0.00254254723900535[/C][C]0.00508509447801069[/C][C]0.997457452760995[/C][/ROW]
[ROW][C]47[/C][C]0.0050037942149748[/C][C]0.0100075884299496[/C][C]0.994996205785025[/C][/ROW]
[ROW][C]48[/C][C]0.0164291107207545[/C][C]0.0328582214415089[/C][C]0.983570889279246[/C][/ROW]
[ROW][C]49[/C][C]0.0680893785913216[/C][C]0.136178757182643[/C][C]0.931910621408678[/C][/ROW]
[ROW][C]50[/C][C]0.276385333526188[/C][C]0.552770667052376[/C][C]0.723614666473812[/C][/ROW]
[ROW][C]51[/C][C]0.36007785048039[/C][C]0.72015570096078[/C][C]0.63992214951961[/C][/ROW]
[ROW][C]52[/C][C]0.761874145322949[/C][C]0.476251709354102[/C][C]0.238125854677051[/C][/ROW]
[ROW][C]53[/C][C]0.7756739101057[/C][C]0.448652179788599[/C][C]0.224326089894299[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58045&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58045&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
160.2716170705965620.5432341411931240.728382929403438
170.1385417686600630.2770835373201270.861458231339937
180.0789277840534550.157855568106910.921072215946545
190.0435419700634680.0870839401269360.956458029936532
200.02961192281306470.05922384562612950.970388077186935
210.02744160828111000.05488321656222010.97255839171889
220.01463192903526570.02926385807053140.985368070964734
230.006989828690178010.01397965738035600.993010171309822
240.003305795466800390.006611590933600770.9966942045332
250.001541738122440750.003083476244881510.99845826187756
260.0006918481911994950.001383696382398990.9993081518088
270.0006825030463087330.001365006092617470.999317496953691
280.000333232141373090.000666464282746180.999666767858627
290.0001605964631323660.0003211929262647330.999839403536868
309.2026202691436e-050.0001840524053828720.999907973797309
315.37366783881139e-050.0001074733567762280.999946263321612
322.97596600300527e-055.95193200601055e-050.99997024033997
331.81012770883963e-053.62025541767927e-050.999981898722912
341.15872292575225e-052.31744585150451e-050.999988412770742
354.21209320490344e-068.42418640980689e-060.999995787906795
361.50500642268526e-063.01001284537052e-060.999998494993577
376.54242088049186e-071.30848417609837e-060.999999345757912
382.80614837912025e-075.6122967582405e-070.999999719385162
391.29979638438219e-072.59959276876439e-070.999999870020362
407.60870347538039e-081.52174069507608e-070.999999923912965
411.96674797758102e-073.93349595516204e-070.999999803325202
426.42757174953938e-071.28551434990788e-060.999999357242825
432.16452600486395e-064.32905200972791e-060.999997835473995
442.65473640077253e-055.30947280154506e-050.999973452635992
450.0004498909005878290.0008997818011756580.999550109099412
460.002542547239005350.005085094478010690.997457452760995
470.00500379421497480.01000758842994960.994996205785025
480.01642911072075450.03285822144150890.983570889279246
490.06808937859132160.1361787571826430.931910621408678
500.2763853335261880.5527706670523760.723614666473812
510.360077850480390.720155700960780.63992214951961
520.7618741453229490.4762517093541020.238125854677051
530.77567391010570.4486521797885990.224326089894299







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level230.605263157894737NOK
5% type I error level270.710526315789474NOK
10% type I error level300.789473684210526NOK

\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 & 23 & 0.605263157894737 & NOK \tabularnewline
5% type I error level & 27 & 0.710526315789474 & NOK \tabularnewline
10% type I error level & 30 & 0.789473684210526 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58045&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]23[/C][C]0.605263157894737[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]27[/C][C]0.710526315789474[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]30[/C][C]0.789473684210526[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58045&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58045&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 level230.605263157894737NOK
5% type I error level270.710526315789474NOK
10% type I error level300.789473684210526NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}