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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 13:45:31 -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/t12587500783c6a566s4f6j840.htm/, Retrieved Thu, 28 Mar 2024 22:51:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58460, Retrieved Thu, 28 Mar 2024 22:51:57 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsWS 7-1
Estimated Impact145
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] [ws 7] [2009-11-19 15:55:06] [b5908418e3090fddbd22f5f0f774653d]
-    D        [Multiple Regression] [Workshop 7-1] [2009-11-20 20:45:31] [a53416c107f5e7e1e12bb9940270d09d] [Current]
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Dataseries X:
9.3	98.3
9.3	112.3
8.7	113.9
8.2	106.2
8.3	98.6
8.5	96.5
8.6	95.9
8.5	103.7
8.2	103.1
8.1	103.7
7.9	112.1
8.6	86.9
8.7	95
8.7	111.8
8.5	108.8
8.4	109.3
8.5	101.4
8.7	100.5
8.7	100.7
8.6	113.5
8.5	106.1
8.3	111.6
8	114.9
8.2	88.6
8.1	99.5
8.1	115.1
8	118
7.9	111.4
7.9	107.3
8	105.3
8	105.3
7.9	117.9
8	110.2
7.7	112.4
7.2	117.5
7.5	93
7.3	103.5
7	116.3
7	120
7	114.3
7.2	104.7
7.3	109.8
7.1	112.6
6.8	114.4
6.4	115.7
6.1	114.7
6.5	118.4
7.7	94.9
7.9	103.8
7.5	115.1
6.9	113.7
6.6	104
6.9	94.3
7.7	92.5
8	93.2
8	104.7
7.7	94
7.3	98.1
7.4	102.7
8.1	82.4




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58460&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58460&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58460&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 time5 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 10.5509226624105 -0.0254339243934731X[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  10.5509226624105 -0.0254339243934731X[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58460&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  10.5509226624105 -0.0254339243934731X[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58460&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58460&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] = + 10.5509226624105 -0.0254339243934731X[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)10.55092266241051.04522810.094400
X-0.02543392439347310.009851-2.58190.0123730.006187

\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) & 10.5509226624105 & 1.045228 & 10.0944 & 0 & 0 \tabularnewline
X & -0.0254339243934731 & 0.009851 & -2.5819 & 0.012373 & 0.006187 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58460&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]10.5509226624105[/C][C]1.045228[/C][C]10.0944[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-0.0254339243934731[/C][C]0.009851[/C][C]-2.5819[/C][C]0.012373[/C][C]0.006187[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58460&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58460&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)10.55092266241051.04522810.094400
X-0.02543392439347310.009851-2.58190.0123730.006187







Multiple Linear Regression - Regression Statistics
Multiple R0.321072294028018
R-squared0.103087417992414
Adjusted R-squared0.0876234079578005
F-TEST (value)6.66627981756805
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0.0123734938424256
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.676353286019171
Sum Squared Residuals26.5323185155180

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.321072294028018 \tabularnewline
R-squared & 0.103087417992414 \tabularnewline
Adjusted R-squared & 0.0876234079578005 \tabularnewline
F-TEST (value) & 6.66627981756805 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 0.0123734938424256 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.676353286019171 \tabularnewline
Sum Squared Residuals & 26.5323185155180 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58460&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.321072294028018[/C][/ROW]
[ROW][C]R-squared[/C][C]0.103087417992414[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0876234079578005[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]6.66627981756805[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]0.0123734938424256[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.676353286019171[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]26.5323185155180[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58460&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58460&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 R0.321072294028018
R-squared0.103087417992414
Adjusted R-squared0.0876234079578005
F-TEST (value)6.66627981756805
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0.0123734938424256
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.676353286019171
Sum Squared Residuals26.5323185155180







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
19.38.05076789453221.24923210546781
29.37.694692953023521.60530704697648
38.77.653998673993961.04600132600604
48.27.84983989182370.350160108176299
58.38.04313771721410.256862282785904
68.58.09654895844040.40345104155961
78.68.111809313076470.488190686923526
88.57.913424702807380.586575297192617
98.27.928685057443470.271314942556532
108.17.913424702807380.186575297192616
117.97.699779737902210.200220262097791
128.68.340714632617730.259285367382268
138.78.13469984503060.5653001549694
148.77.707409915220250.992590084779748
158.57.783711688400670.71628831159933
168.47.770994726203930.629005273796066
178.57.971922728912370.528077271087628
188.77.99481326086650.705186739133502
198.77.98972647598780.710273524012197
208.67.664172243751350.935827756248653
218.57.852383284263050.647616715736952
228.37.712496700098950.587503299901055
2387.628564749600480.371435250399516
248.28.29747696114883-0.0974769611488285
258.18.020247185259970.079752814740029
268.17.623477964721790.47652203527821
2787.549719583980720.450280416019282
287.97.717583484977640.18241651502236
297.97.821862574990880.07813742500912
3087.872730423777830.127269576222173
3187.872730423777830.127269576222173
327.97.552262976420070.347737023579935
3387.748104194249810.251895805750192
347.77.692149560584170.007850439415833
357.27.56243654617745-0.362436546177454
367.58.18556769381755-0.685567693817546
377.37.91851148768608-0.618511487686078
3877.59295725544962-0.592957255449622
3977.49885173519377-0.498851735193772
4077.64382510423657-0.643825104236568
417.27.88799077841391-0.68799077841391
427.37.7582777640072-0.458277764007198
437.17.68706277570547-0.587062775705473
446.87.64128171179722-0.841281711797221
456.47.6082176100857-1.20821761008571
466.17.63365153447918-1.53365153447918
476.57.53954601422333-1.03954601422333
487.78.13724323746995-0.437243237469947
497.97.91088131036804-0.0108813103680359
507.57.62347796472179-0.12347796472179
516.97.65908545887265-0.759085458872652
526.67.90579452548934-1.30579452548934
536.98.15250359210603-1.25250359210603
547.78.19828465601428-0.498284656014282
5588.18048090893885-0.180480908938851
5687.887990778413910.112009221586090
577.78.16013376942407-0.460133769424073
587.38.05585467941083-0.755854679410833
597.47.93885862720086-0.538858627200856
608.18.45516729238836-0.355167292388361

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 9.3 & 8.0507678945322 & 1.24923210546781 \tabularnewline
2 & 9.3 & 7.69469295302352 & 1.60530704697648 \tabularnewline
3 & 8.7 & 7.65399867399396 & 1.04600132600604 \tabularnewline
4 & 8.2 & 7.8498398918237 & 0.350160108176299 \tabularnewline
5 & 8.3 & 8.0431377172141 & 0.256862282785904 \tabularnewline
6 & 8.5 & 8.0965489584404 & 0.40345104155961 \tabularnewline
7 & 8.6 & 8.11180931307647 & 0.488190686923526 \tabularnewline
8 & 8.5 & 7.91342470280738 & 0.586575297192617 \tabularnewline
9 & 8.2 & 7.92868505744347 & 0.271314942556532 \tabularnewline
10 & 8.1 & 7.91342470280738 & 0.186575297192616 \tabularnewline
11 & 7.9 & 7.69977973790221 & 0.200220262097791 \tabularnewline
12 & 8.6 & 8.34071463261773 & 0.259285367382268 \tabularnewline
13 & 8.7 & 8.1346998450306 & 0.5653001549694 \tabularnewline
14 & 8.7 & 7.70740991522025 & 0.992590084779748 \tabularnewline
15 & 8.5 & 7.78371168840067 & 0.71628831159933 \tabularnewline
16 & 8.4 & 7.77099472620393 & 0.629005273796066 \tabularnewline
17 & 8.5 & 7.97192272891237 & 0.528077271087628 \tabularnewline
18 & 8.7 & 7.9948132608665 & 0.705186739133502 \tabularnewline
19 & 8.7 & 7.9897264759878 & 0.710273524012197 \tabularnewline
20 & 8.6 & 7.66417224375135 & 0.935827756248653 \tabularnewline
21 & 8.5 & 7.85238328426305 & 0.647616715736952 \tabularnewline
22 & 8.3 & 7.71249670009895 & 0.587503299901055 \tabularnewline
23 & 8 & 7.62856474960048 & 0.371435250399516 \tabularnewline
24 & 8.2 & 8.29747696114883 & -0.0974769611488285 \tabularnewline
25 & 8.1 & 8.02024718525997 & 0.079752814740029 \tabularnewline
26 & 8.1 & 7.62347796472179 & 0.47652203527821 \tabularnewline
27 & 8 & 7.54971958398072 & 0.450280416019282 \tabularnewline
28 & 7.9 & 7.71758348497764 & 0.18241651502236 \tabularnewline
29 & 7.9 & 7.82186257499088 & 0.07813742500912 \tabularnewline
30 & 8 & 7.87273042377783 & 0.127269576222173 \tabularnewline
31 & 8 & 7.87273042377783 & 0.127269576222173 \tabularnewline
32 & 7.9 & 7.55226297642007 & 0.347737023579935 \tabularnewline
33 & 8 & 7.74810419424981 & 0.251895805750192 \tabularnewline
34 & 7.7 & 7.69214956058417 & 0.007850439415833 \tabularnewline
35 & 7.2 & 7.56243654617745 & -0.362436546177454 \tabularnewline
36 & 7.5 & 8.18556769381755 & -0.685567693817546 \tabularnewline
37 & 7.3 & 7.91851148768608 & -0.618511487686078 \tabularnewline
38 & 7 & 7.59295725544962 & -0.592957255449622 \tabularnewline
39 & 7 & 7.49885173519377 & -0.498851735193772 \tabularnewline
40 & 7 & 7.64382510423657 & -0.643825104236568 \tabularnewline
41 & 7.2 & 7.88799077841391 & -0.68799077841391 \tabularnewline
42 & 7.3 & 7.7582777640072 & -0.458277764007198 \tabularnewline
43 & 7.1 & 7.68706277570547 & -0.587062775705473 \tabularnewline
44 & 6.8 & 7.64128171179722 & -0.841281711797221 \tabularnewline
45 & 6.4 & 7.6082176100857 & -1.20821761008571 \tabularnewline
46 & 6.1 & 7.63365153447918 & -1.53365153447918 \tabularnewline
47 & 6.5 & 7.53954601422333 & -1.03954601422333 \tabularnewline
48 & 7.7 & 8.13724323746995 & -0.437243237469947 \tabularnewline
49 & 7.9 & 7.91088131036804 & -0.0108813103680359 \tabularnewline
50 & 7.5 & 7.62347796472179 & -0.12347796472179 \tabularnewline
51 & 6.9 & 7.65908545887265 & -0.759085458872652 \tabularnewline
52 & 6.6 & 7.90579452548934 & -1.30579452548934 \tabularnewline
53 & 6.9 & 8.15250359210603 & -1.25250359210603 \tabularnewline
54 & 7.7 & 8.19828465601428 & -0.498284656014282 \tabularnewline
55 & 8 & 8.18048090893885 & -0.180480908938851 \tabularnewline
56 & 8 & 7.88799077841391 & 0.112009221586090 \tabularnewline
57 & 7.7 & 8.16013376942407 & -0.460133769424073 \tabularnewline
58 & 7.3 & 8.05585467941083 & -0.755854679410833 \tabularnewline
59 & 7.4 & 7.93885862720086 & -0.538858627200856 \tabularnewline
60 & 8.1 & 8.45516729238836 & -0.355167292388361 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58460&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]9.3[/C][C]8.0507678945322[/C][C]1.24923210546781[/C][/ROW]
[ROW][C]2[/C][C]9.3[/C][C]7.69469295302352[/C][C]1.60530704697648[/C][/ROW]
[ROW][C]3[/C][C]8.7[/C][C]7.65399867399396[/C][C]1.04600132600604[/C][/ROW]
[ROW][C]4[/C][C]8.2[/C][C]7.8498398918237[/C][C]0.350160108176299[/C][/ROW]
[ROW][C]5[/C][C]8.3[/C][C]8.0431377172141[/C][C]0.256862282785904[/C][/ROW]
[ROW][C]6[/C][C]8.5[/C][C]8.0965489584404[/C][C]0.40345104155961[/C][/ROW]
[ROW][C]7[/C][C]8.6[/C][C]8.11180931307647[/C][C]0.488190686923526[/C][/ROW]
[ROW][C]8[/C][C]8.5[/C][C]7.91342470280738[/C][C]0.586575297192617[/C][/ROW]
[ROW][C]9[/C][C]8.2[/C][C]7.92868505744347[/C][C]0.271314942556532[/C][/ROW]
[ROW][C]10[/C][C]8.1[/C][C]7.91342470280738[/C][C]0.186575297192616[/C][/ROW]
[ROW][C]11[/C][C]7.9[/C][C]7.69977973790221[/C][C]0.200220262097791[/C][/ROW]
[ROW][C]12[/C][C]8.6[/C][C]8.34071463261773[/C][C]0.259285367382268[/C][/ROW]
[ROW][C]13[/C][C]8.7[/C][C]8.1346998450306[/C][C]0.5653001549694[/C][/ROW]
[ROW][C]14[/C][C]8.7[/C][C]7.70740991522025[/C][C]0.992590084779748[/C][/ROW]
[ROW][C]15[/C][C]8.5[/C][C]7.78371168840067[/C][C]0.71628831159933[/C][/ROW]
[ROW][C]16[/C][C]8.4[/C][C]7.77099472620393[/C][C]0.629005273796066[/C][/ROW]
[ROW][C]17[/C][C]8.5[/C][C]7.97192272891237[/C][C]0.528077271087628[/C][/ROW]
[ROW][C]18[/C][C]8.7[/C][C]7.9948132608665[/C][C]0.705186739133502[/C][/ROW]
[ROW][C]19[/C][C]8.7[/C][C]7.9897264759878[/C][C]0.710273524012197[/C][/ROW]
[ROW][C]20[/C][C]8.6[/C][C]7.66417224375135[/C][C]0.935827756248653[/C][/ROW]
[ROW][C]21[/C][C]8.5[/C][C]7.85238328426305[/C][C]0.647616715736952[/C][/ROW]
[ROW][C]22[/C][C]8.3[/C][C]7.71249670009895[/C][C]0.587503299901055[/C][/ROW]
[ROW][C]23[/C][C]8[/C][C]7.62856474960048[/C][C]0.371435250399516[/C][/ROW]
[ROW][C]24[/C][C]8.2[/C][C]8.29747696114883[/C][C]-0.0974769611488285[/C][/ROW]
[ROW][C]25[/C][C]8.1[/C][C]8.02024718525997[/C][C]0.079752814740029[/C][/ROW]
[ROW][C]26[/C][C]8.1[/C][C]7.62347796472179[/C][C]0.47652203527821[/C][/ROW]
[ROW][C]27[/C][C]8[/C][C]7.54971958398072[/C][C]0.450280416019282[/C][/ROW]
[ROW][C]28[/C][C]7.9[/C][C]7.71758348497764[/C][C]0.18241651502236[/C][/ROW]
[ROW][C]29[/C][C]7.9[/C][C]7.82186257499088[/C][C]0.07813742500912[/C][/ROW]
[ROW][C]30[/C][C]8[/C][C]7.87273042377783[/C][C]0.127269576222173[/C][/ROW]
[ROW][C]31[/C][C]8[/C][C]7.87273042377783[/C][C]0.127269576222173[/C][/ROW]
[ROW][C]32[/C][C]7.9[/C][C]7.55226297642007[/C][C]0.347737023579935[/C][/ROW]
[ROW][C]33[/C][C]8[/C][C]7.74810419424981[/C][C]0.251895805750192[/C][/ROW]
[ROW][C]34[/C][C]7.7[/C][C]7.69214956058417[/C][C]0.007850439415833[/C][/ROW]
[ROW][C]35[/C][C]7.2[/C][C]7.56243654617745[/C][C]-0.362436546177454[/C][/ROW]
[ROW][C]36[/C][C]7.5[/C][C]8.18556769381755[/C][C]-0.685567693817546[/C][/ROW]
[ROW][C]37[/C][C]7.3[/C][C]7.91851148768608[/C][C]-0.618511487686078[/C][/ROW]
[ROW][C]38[/C][C]7[/C][C]7.59295725544962[/C][C]-0.592957255449622[/C][/ROW]
[ROW][C]39[/C][C]7[/C][C]7.49885173519377[/C][C]-0.498851735193772[/C][/ROW]
[ROW][C]40[/C][C]7[/C][C]7.64382510423657[/C][C]-0.643825104236568[/C][/ROW]
[ROW][C]41[/C][C]7.2[/C][C]7.88799077841391[/C][C]-0.68799077841391[/C][/ROW]
[ROW][C]42[/C][C]7.3[/C][C]7.7582777640072[/C][C]-0.458277764007198[/C][/ROW]
[ROW][C]43[/C][C]7.1[/C][C]7.68706277570547[/C][C]-0.587062775705473[/C][/ROW]
[ROW][C]44[/C][C]6.8[/C][C]7.64128171179722[/C][C]-0.841281711797221[/C][/ROW]
[ROW][C]45[/C][C]6.4[/C][C]7.6082176100857[/C][C]-1.20821761008571[/C][/ROW]
[ROW][C]46[/C][C]6.1[/C][C]7.63365153447918[/C][C]-1.53365153447918[/C][/ROW]
[ROW][C]47[/C][C]6.5[/C][C]7.53954601422333[/C][C]-1.03954601422333[/C][/ROW]
[ROW][C]48[/C][C]7.7[/C][C]8.13724323746995[/C][C]-0.437243237469947[/C][/ROW]
[ROW][C]49[/C][C]7.9[/C][C]7.91088131036804[/C][C]-0.0108813103680359[/C][/ROW]
[ROW][C]50[/C][C]7.5[/C][C]7.62347796472179[/C][C]-0.12347796472179[/C][/ROW]
[ROW][C]51[/C][C]6.9[/C][C]7.65908545887265[/C][C]-0.759085458872652[/C][/ROW]
[ROW][C]52[/C][C]6.6[/C][C]7.90579452548934[/C][C]-1.30579452548934[/C][/ROW]
[ROW][C]53[/C][C]6.9[/C][C]8.15250359210603[/C][C]-1.25250359210603[/C][/ROW]
[ROW][C]54[/C][C]7.7[/C][C]8.19828465601428[/C][C]-0.498284656014282[/C][/ROW]
[ROW][C]55[/C][C]8[/C][C]8.18048090893885[/C][C]-0.180480908938851[/C][/ROW]
[ROW][C]56[/C][C]8[/C][C]7.88799077841391[/C][C]0.112009221586090[/C][/ROW]
[ROW][C]57[/C][C]7.7[/C][C]8.16013376942407[/C][C]-0.460133769424073[/C][/ROW]
[ROW][C]58[/C][C]7.3[/C][C]8.05585467941083[/C][C]-0.755854679410833[/C][/ROW]
[ROW][C]59[/C][C]7.4[/C][C]7.93885862720086[/C][C]-0.538858627200856[/C][/ROW]
[ROW][C]60[/C][C]8.1[/C][C]8.45516729238836[/C][C]-0.355167292388361[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58460&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58460&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
19.38.05076789453221.24923210546781
29.37.694692953023521.60530704697648
38.77.653998673993961.04600132600604
48.27.84983989182370.350160108176299
58.38.04313771721410.256862282785904
68.58.09654895844040.40345104155961
78.68.111809313076470.488190686923526
88.57.913424702807380.586575297192617
98.27.928685057443470.271314942556532
108.17.913424702807380.186575297192616
117.97.699779737902210.200220262097791
128.68.340714632617730.259285367382268
138.78.13469984503060.5653001549694
148.77.707409915220250.992590084779748
158.57.783711688400670.71628831159933
168.47.770994726203930.629005273796066
178.57.971922728912370.528077271087628
188.77.99481326086650.705186739133502
198.77.98972647598780.710273524012197
208.67.664172243751350.935827756248653
218.57.852383284263050.647616715736952
228.37.712496700098950.587503299901055
2387.628564749600480.371435250399516
248.28.29747696114883-0.0974769611488285
258.18.020247185259970.079752814740029
268.17.623477964721790.47652203527821
2787.549719583980720.450280416019282
287.97.717583484977640.18241651502236
297.97.821862574990880.07813742500912
3087.872730423777830.127269576222173
3187.872730423777830.127269576222173
327.97.552262976420070.347737023579935
3387.748104194249810.251895805750192
347.77.692149560584170.007850439415833
357.27.56243654617745-0.362436546177454
367.58.18556769381755-0.685567693817546
377.37.91851148768608-0.618511487686078
3877.59295725544962-0.592957255449622
3977.49885173519377-0.498851735193772
4077.64382510423657-0.643825104236568
417.27.88799077841391-0.68799077841391
427.37.7582777640072-0.458277764007198
437.17.68706277570547-0.587062775705473
446.87.64128171179722-0.841281711797221
456.47.6082176100857-1.20821761008571
466.17.63365153447918-1.53365153447918
476.57.53954601422333-1.03954601422333
487.78.13724323746995-0.437243237469947
497.97.91088131036804-0.0108813103680359
507.57.62347796472179-0.12347796472179
516.97.65908545887265-0.759085458872652
526.67.90579452548934-1.30579452548934
536.98.15250359210603-1.25250359210603
547.78.19828465601428-0.498284656014282
5588.18048090893885-0.180480908938851
5687.887990778413910.112009221586090
577.78.16013376942407-0.460133769424073
587.38.05585467941083-0.755854679410833
597.47.93885862720086-0.538858627200856
608.18.45516729238836-0.355167292388361







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.5677492812014770.8645014375970470.432250718798523
60.4073433597458860.8146867194917720.592656640254114
70.2713362781143780.5426725562287570.728663721885622
80.1846769996146560.3693539992293120.815323000385344
90.1580564476002810.3161128952005630.841943552399719
100.1458937785834850.2917875571669690.854106221416515
110.1768426693159600.3536853386319190.82315733068404
120.1173940061766390.2347880123532780.882605993823361
130.0829265611701280.1658531223402560.917073438829872
140.06904668532355810.1380933706471160.930953314676442
150.04998957666374980.09997915332749950.95001042333625
160.03624644710711140.07249289421422280.963753552892889
170.02508426827304310.05016853654608620.974915731726957
180.02073856741136590.04147713482273190.979261432588634
190.01828716090416480.03657432180832950.981712839095835
200.01951941566712230.03903883133424470.980480584332878
210.01806302618127310.03612605236254620.981936973818727
220.01855147799271210.03710295598542420.981448522007288
230.02408641416880330.04817282833760650.975913585831197
240.02276677861234510.04553355722469030.977233221387655
250.02334280101312780.04668560202625570.976657198986872
260.02942884914612380.05885769829224750.970571150853876
270.04161445709745530.08322891419491060.958385542902545
280.05538096302781870.1107619260556370.944619036972181
290.06907719973904170.1381543994780830.930922800260958
300.08077750995047880.1615550199009580.919222490049521
310.09629966145071030.1925993229014210.90370033854929
320.159668273715930.319336547431860.84033172628407
330.2419701888430750.4839403776861510.758029811156925
340.3421458986158520.6842917972317030.657854101384148
350.4833030361241540.9666060722483080.516696963875846
360.6019677913278460.7960644173443090.398032208672154
370.6695365098761570.6609269802476850.330463490123843
380.7324388393771790.5351223212456430.267561160622821
390.7648273820942260.4703452358115490.235172617905774
400.7771327425359540.4457345149280910.222867257464046
410.7786622955497670.4426754089004670.221337704450234
420.7617324963605640.4765350072788730.238267503639436
430.7426283186084210.5147433627831590.257371681391579
440.7295321242369920.5409357515260160.270467875763008
450.7708178634242350.4583642731515310.229182136575765
460.9025072690308370.1949854619383260.097492730969163
470.9077319897840930.1845360204318140.0922680102159068
480.868326018310540.2633479633789210.131673981689460
490.854181787485460.2916364250290790.145818212514540
500.8393038593414350.3213922813171290.160696140658565
510.7663612659177340.4672774681645320.233638734082266
520.8680699758751920.2638600482496170.131930024124808
530.965360743484330.06927851303133860.0346392565156693
540.9197353176643220.1605293646713560.0802646823356778
550.8401291678294630.3197416643410750.159870832170537

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.567749281201477 & 0.864501437597047 & 0.432250718798523 \tabularnewline
6 & 0.407343359745886 & 0.814686719491772 & 0.592656640254114 \tabularnewline
7 & 0.271336278114378 & 0.542672556228757 & 0.728663721885622 \tabularnewline
8 & 0.184676999614656 & 0.369353999229312 & 0.815323000385344 \tabularnewline
9 & 0.158056447600281 & 0.316112895200563 & 0.841943552399719 \tabularnewline
10 & 0.145893778583485 & 0.291787557166969 & 0.854106221416515 \tabularnewline
11 & 0.176842669315960 & 0.353685338631919 & 0.82315733068404 \tabularnewline
12 & 0.117394006176639 & 0.234788012353278 & 0.882605993823361 \tabularnewline
13 & 0.082926561170128 & 0.165853122340256 & 0.917073438829872 \tabularnewline
14 & 0.0690466853235581 & 0.138093370647116 & 0.930953314676442 \tabularnewline
15 & 0.0499895766637498 & 0.0999791533274995 & 0.95001042333625 \tabularnewline
16 & 0.0362464471071114 & 0.0724928942142228 & 0.963753552892889 \tabularnewline
17 & 0.0250842682730431 & 0.0501685365460862 & 0.974915731726957 \tabularnewline
18 & 0.0207385674113659 & 0.0414771348227319 & 0.979261432588634 \tabularnewline
19 & 0.0182871609041648 & 0.0365743218083295 & 0.981712839095835 \tabularnewline
20 & 0.0195194156671223 & 0.0390388313342447 & 0.980480584332878 \tabularnewline
21 & 0.0180630261812731 & 0.0361260523625462 & 0.981936973818727 \tabularnewline
22 & 0.0185514779927121 & 0.0371029559854242 & 0.981448522007288 \tabularnewline
23 & 0.0240864141688033 & 0.0481728283376065 & 0.975913585831197 \tabularnewline
24 & 0.0227667786123451 & 0.0455335572246903 & 0.977233221387655 \tabularnewline
25 & 0.0233428010131278 & 0.0466856020262557 & 0.976657198986872 \tabularnewline
26 & 0.0294288491461238 & 0.0588576982922475 & 0.970571150853876 \tabularnewline
27 & 0.0416144570974553 & 0.0832289141949106 & 0.958385542902545 \tabularnewline
28 & 0.0553809630278187 & 0.110761926055637 & 0.944619036972181 \tabularnewline
29 & 0.0690771997390417 & 0.138154399478083 & 0.930922800260958 \tabularnewline
30 & 0.0807775099504788 & 0.161555019900958 & 0.919222490049521 \tabularnewline
31 & 0.0962996614507103 & 0.192599322901421 & 0.90370033854929 \tabularnewline
32 & 0.15966827371593 & 0.31933654743186 & 0.84033172628407 \tabularnewline
33 & 0.241970188843075 & 0.483940377686151 & 0.758029811156925 \tabularnewline
34 & 0.342145898615852 & 0.684291797231703 & 0.657854101384148 \tabularnewline
35 & 0.483303036124154 & 0.966606072248308 & 0.516696963875846 \tabularnewline
36 & 0.601967791327846 & 0.796064417344309 & 0.398032208672154 \tabularnewline
37 & 0.669536509876157 & 0.660926980247685 & 0.330463490123843 \tabularnewline
38 & 0.732438839377179 & 0.535122321245643 & 0.267561160622821 \tabularnewline
39 & 0.764827382094226 & 0.470345235811549 & 0.235172617905774 \tabularnewline
40 & 0.777132742535954 & 0.445734514928091 & 0.222867257464046 \tabularnewline
41 & 0.778662295549767 & 0.442675408900467 & 0.221337704450234 \tabularnewline
42 & 0.761732496360564 & 0.476535007278873 & 0.238267503639436 \tabularnewline
43 & 0.742628318608421 & 0.514743362783159 & 0.257371681391579 \tabularnewline
44 & 0.729532124236992 & 0.540935751526016 & 0.270467875763008 \tabularnewline
45 & 0.770817863424235 & 0.458364273151531 & 0.229182136575765 \tabularnewline
46 & 0.902507269030837 & 0.194985461938326 & 0.097492730969163 \tabularnewline
47 & 0.907731989784093 & 0.184536020431814 & 0.0922680102159068 \tabularnewline
48 & 0.86832601831054 & 0.263347963378921 & 0.131673981689460 \tabularnewline
49 & 0.85418178748546 & 0.291636425029079 & 0.145818212514540 \tabularnewline
50 & 0.839303859341435 & 0.321392281317129 & 0.160696140658565 \tabularnewline
51 & 0.766361265917734 & 0.467277468164532 & 0.233638734082266 \tabularnewline
52 & 0.868069975875192 & 0.263860048249617 & 0.131930024124808 \tabularnewline
53 & 0.96536074348433 & 0.0692785130313386 & 0.0346392565156693 \tabularnewline
54 & 0.919735317664322 & 0.160529364671356 & 0.0802646823356778 \tabularnewline
55 & 0.840129167829463 & 0.319741664341075 & 0.159870832170537 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58460&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]5[/C][C]0.567749281201477[/C][C]0.864501437597047[/C][C]0.432250718798523[/C][/ROW]
[ROW][C]6[/C][C]0.407343359745886[/C][C]0.814686719491772[/C][C]0.592656640254114[/C][/ROW]
[ROW][C]7[/C][C]0.271336278114378[/C][C]0.542672556228757[/C][C]0.728663721885622[/C][/ROW]
[ROW][C]8[/C][C]0.184676999614656[/C][C]0.369353999229312[/C][C]0.815323000385344[/C][/ROW]
[ROW][C]9[/C][C]0.158056447600281[/C][C]0.316112895200563[/C][C]0.841943552399719[/C][/ROW]
[ROW][C]10[/C][C]0.145893778583485[/C][C]0.291787557166969[/C][C]0.854106221416515[/C][/ROW]
[ROW][C]11[/C][C]0.176842669315960[/C][C]0.353685338631919[/C][C]0.82315733068404[/C][/ROW]
[ROW][C]12[/C][C]0.117394006176639[/C][C]0.234788012353278[/C][C]0.882605993823361[/C][/ROW]
[ROW][C]13[/C][C]0.082926561170128[/C][C]0.165853122340256[/C][C]0.917073438829872[/C][/ROW]
[ROW][C]14[/C][C]0.0690466853235581[/C][C]0.138093370647116[/C][C]0.930953314676442[/C][/ROW]
[ROW][C]15[/C][C]0.0499895766637498[/C][C]0.0999791533274995[/C][C]0.95001042333625[/C][/ROW]
[ROW][C]16[/C][C]0.0362464471071114[/C][C]0.0724928942142228[/C][C]0.963753552892889[/C][/ROW]
[ROW][C]17[/C][C]0.0250842682730431[/C][C]0.0501685365460862[/C][C]0.974915731726957[/C][/ROW]
[ROW][C]18[/C][C]0.0207385674113659[/C][C]0.0414771348227319[/C][C]0.979261432588634[/C][/ROW]
[ROW][C]19[/C][C]0.0182871609041648[/C][C]0.0365743218083295[/C][C]0.981712839095835[/C][/ROW]
[ROW][C]20[/C][C]0.0195194156671223[/C][C]0.0390388313342447[/C][C]0.980480584332878[/C][/ROW]
[ROW][C]21[/C][C]0.0180630261812731[/C][C]0.0361260523625462[/C][C]0.981936973818727[/C][/ROW]
[ROW][C]22[/C][C]0.0185514779927121[/C][C]0.0371029559854242[/C][C]0.981448522007288[/C][/ROW]
[ROW][C]23[/C][C]0.0240864141688033[/C][C]0.0481728283376065[/C][C]0.975913585831197[/C][/ROW]
[ROW][C]24[/C][C]0.0227667786123451[/C][C]0.0455335572246903[/C][C]0.977233221387655[/C][/ROW]
[ROW][C]25[/C][C]0.0233428010131278[/C][C]0.0466856020262557[/C][C]0.976657198986872[/C][/ROW]
[ROW][C]26[/C][C]0.0294288491461238[/C][C]0.0588576982922475[/C][C]0.970571150853876[/C][/ROW]
[ROW][C]27[/C][C]0.0416144570974553[/C][C]0.0832289141949106[/C][C]0.958385542902545[/C][/ROW]
[ROW][C]28[/C][C]0.0553809630278187[/C][C]0.110761926055637[/C][C]0.944619036972181[/C][/ROW]
[ROW][C]29[/C][C]0.0690771997390417[/C][C]0.138154399478083[/C][C]0.930922800260958[/C][/ROW]
[ROW][C]30[/C][C]0.0807775099504788[/C][C]0.161555019900958[/C][C]0.919222490049521[/C][/ROW]
[ROW][C]31[/C][C]0.0962996614507103[/C][C]0.192599322901421[/C][C]0.90370033854929[/C][/ROW]
[ROW][C]32[/C][C]0.15966827371593[/C][C]0.31933654743186[/C][C]0.84033172628407[/C][/ROW]
[ROW][C]33[/C][C]0.241970188843075[/C][C]0.483940377686151[/C][C]0.758029811156925[/C][/ROW]
[ROW][C]34[/C][C]0.342145898615852[/C][C]0.684291797231703[/C][C]0.657854101384148[/C][/ROW]
[ROW][C]35[/C][C]0.483303036124154[/C][C]0.966606072248308[/C][C]0.516696963875846[/C][/ROW]
[ROW][C]36[/C][C]0.601967791327846[/C][C]0.796064417344309[/C][C]0.398032208672154[/C][/ROW]
[ROW][C]37[/C][C]0.669536509876157[/C][C]0.660926980247685[/C][C]0.330463490123843[/C][/ROW]
[ROW][C]38[/C][C]0.732438839377179[/C][C]0.535122321245643[/C][C]0.267561160622821[/C][/ROW]
[ROW][C]39[/C][C]0.764827382094226[/C][C]0.470345235811549[/C][C]0.235172617905774[/C][/ROW]
[ROW][C]40[/C][C]0.777132742535954[/C][C]0.445734514928091[/C][C]0.222867257464046[/C][/ROW]
[ROW][C]41[/C][C]0.778662295549767[/C][C]0.442675408900467[/C][C]0.221337704450234[/C][/ROW]
[ROW][C]42[/C][C]0.761732496360564[/C][C]0.476535007278873[/C][C]0.238267503639436[/C][/ROW]
[ROW][C]43[/C][C]0.742628318608421[/C][C]0.514743362783159[/C][C]0.257371681391579[/C][/ROW]
[ROW][C]44[/C][C]0.729532124236992[/C][C]0.540935751526016[/C][C]0.270467875763008[/C][/ROW]
[ROW][C]45[/C][C]0.770817863424235[/C][C]0.458364273151531[/C][C]0.229182136575765[/C][/ROW]
[ROW][C]46[/C][C]0.902507269030837[/C][C]0.194985461938326[/C][C]0.097492730969163[/C][/ROW]
[ROW][C]47[/C][C]0.907731989784093[/C][C]0.184536020431814[/C][C]0.0922680102159068[/C][/ROW]
[ROW][C]48[/C][C]0.86832601831054[/C][C]0.263347963378921[/C][C]0.131673981689460[/C][/ROW]
[ROW][C]49[/C][C]0.85418178748546[/C][C]0.291636425029079[/C][C]0.145818212514540[/C][/ROW]
[ROW][C]50[/C][C]0.839303859341435[/C][C]0.321392281317129[/C][C]0.160696140658565[/C][/ROW]
[ROW][C]51[/C][C]0.766361265917734[/C][C]0.467277468164532[/C][C]0.233638734082266[/C][/ROW]
[ROW][C]52[/C][C]0.868069975875192[/C][C]0.263860048249617[/C][C]0.131930024124808[/C][/ROW]
[ROW][C]53[/C][C]0.96536074348433[/C][C]0.0692785130313386[/C][C]0.0346392565156693[/C][/ROW]
[ROW][C]54[/C][C]0.919735317664322[/C][C]0.160529364671356[/C][C]0.0802646823356778[/C][/ROW]
[ROW][C]55[/C][C]0.840129167829463[/C][C]0.319741664341075[/C][C]0.159870832170537[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58460&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58460&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.5677492812014770.8645014375970470.432250718798523
60.4073433597458860.8146867194917720.592656640254114
70.2713362781143780.5426725562287570.728663721885622
80.1846769996146560.3693539992293120.815323000385344
90.1580564476002810.3161128952005630.841943552399719
100.1458937785834850.2917875571669690.854106221416515
110.1768426693159600.3536853386319190.82315733068404
120.1173940061766390.2347880123532780.882605993823361
130.0829265611701280.1658531223402560.917073438829872
140.06904668532355810.1380933706471160.930953314676442
150.04998957666374980.09997915332749950.95001042333625
160.03624644710711140.07249289421422280.963753552892889
170.02508426827304310.05016853654608620.974915731726957
180.02073856741136590.04147713482273190.979261432588634
190.01828716090416480.03657432180832950.981712839095835
200.01951941566712230.03903883133424470.980480584332878
210.01806302618127310.03612605236254620.981936973818727
220.01855147799271210.03710295598542420.981448522007288
230.02408641416880330.04817282833760650.975913585831197
240.02276677861234510.04553355722469030.977233221387655
250.02334280101312780.04668560202625570.976657198986872
260.02942884914612380.05885769829224750.970571150853876
270.04161445709745530.08322891419491060.958385542902545
280.05538096302781870.1107619260556370.944619036972181
290.06907719973904170.1381543994780830.930922800260958
300.08077750995047880.1615550199009580.919222490049521
310.09629966145071030.1925993229014210.90370033854929
320.159668273715930.319336547431860.84033172628407
330.2419701888430750.4839403776861510.758029811156925
340.3421458986158520.6842917972317030.657854101384148
350.4833030361241540.9666060722483080.516696963875846
360.6019677913278460.7960644173443090.398032208672154
370.6695365098761570.6609269802476850.330463490123843
380.7324388393771790.5351223212456430.267561160622821
390.7648273820942260.4703452358115490.235172617905774
400.7771327425359540.4457345149280910.222867257464046
410.7786622955497670.4426754089004670.221337704450234
420.7617324963605640.4765350072788730.238267503639436
430.7426283186084210.5147433627831590.257371681391579
440.7295321242369920.5409357515260160.270467875763008
450.7708178634242350.4583642731515310.229182136575765
460.9025072690308370.1949854619383260.097492730969163
470.9077319897840930.1845360204318140.0922680102159068
480.868326018310540.2633479633789210.131673981689460
490.854181787485460.2916364250290790.145818212514540
500.8393038593414350.3213922813171290.160696140658565
510.7663612659177340.4672774681645320.233638734082266
520.8680699758751920.2638600482496170.131930024124808
530.965360743484330.06927851303133860.0346392565156693
540.9197353176643220.1605293646713560.0802646823356778
550.8401291678294630.3197416643410750.159870832170537







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level80.156862745098039NOK
10% type I error level140.274509803921569NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 8 & 0.156862745098039 & NOK \tabularnewline
10% type I error level & 14 & 0.274509803921569 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58460&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]8[/C][C]0.156862745098039[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]14[/C][C]0.274509803921569[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58460&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58460&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level80.156862745098039NOK
10% type I error level140.274509803921569NOK



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