Free Statistics

of Irreproducible Research!

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 04:27:26 -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/t12587165231dd23aviu1w2hz6.htm/, Retrieved Thu, 28 Mar 2024 15:50:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58042, Retrieved Thu, 28 Mar 2024 15:50:53 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact173
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] [link 1] [2009-11-20 11:27:26] [454b2df2fae01897bad5ff38ed3cc924] [Current]
-   PD        [Multiple Regression] [] [2009-11-27 13:52:51] [21a312bc02d33649b9f78fd202ca0963]
-   PD        [Multiple Regression] [] [2009-11-27 14:12:05] [21a312bc02d33649b9f78fd202ca0963]
-   PD        [Multiple Regression] [] [2009-11-27 14:22:07] [21a312bc02d33649b9f78fd202ca0963]
-   PD        [Multiple Regression] [] [2009-11-27 14:34:20] [21a312bc02d33649b9f78fd202ca0963]
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Dataseries X:
1,58	0,55
1,59	0,55
1,6	0,55
1,6	0,55
1,6	0,55
1,6	0,56
1,61	0,56
1,61	0,56
1,62	0,56
1,63	0,56
1,63	0,55
1,63	0,56
1,63	0,55
1,63	0,55
1,64	0,56
1,64	0,55
1,64	0,55
1,65	0,55
1,65	0,55
1,65	0,53
1,65	0,53
1,65	0,53
1,66	0,53
1,67	0,54
1,68	0,54
1,68	0,54
1,68	0,55
1,68	0,55
1,69	0,54
1,7	0,55
1,7	0,56
1,71	0,58
1,73	0,59
1,73	0,6
1,73	0,6
1,74	0,6
1,74	0,59
1,74	0,6
1,75	0,6
1,78	0,62
1,82	0,65
1,83	0,68
1,84	0,73
1,85	0,78
1,86	0,78
1,86	0,82
1,87	0,82
1,87	0,81
1,87	0,83
1,87	0,85
1,87	0,86
1,87	0,85
1,87	0,85
1,88	0,82
1,88	0,8
1,87	0,81
1,87	0,8
1,87	0,8
1,87	0,8
1,87	0,8
1,87	0,79




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58042&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58042&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58042&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 1.2035843418843 + 0.823909896346055X[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  1.2035843418843 +  0.823909896346055X[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58042&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  1.2035843418843 +  0.823909896346055X[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58042&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58042&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] = + 1.2035843418843 + 0.823909896346055X[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.20358434188430.02667345.124300
X0.8239098963460550.04090420.142400

\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) & 1.2035843418843 & 0.026673 & 45.1243 & 0 & 0 \tabularnewline
X & 0.823909896346055 & 0.040904 & 20.1424 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58042&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]1.2035843418843[/C][C]0.026673[/C][C]45.1243[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]0.823909896346055[/C][C]0.040904[/C][C]20.1424[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58042&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58042&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)1.20358434188430.02667345.124300
X0.8239098963460550.04090420.142400







Multiple Linear Regression - Regression Statistics
Multiple R0.934366697063404
R-squared0.873041124581175
Adjusted R-squared0.870889279235094
F-TEST (value)405.717411881327
F-TEST (DF numerator)1
F-TEST (DF denominator)59
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0376961676966682
Sum Squared Residuals0.0838390624819047

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.934366697063404 \tabularnewline
R-squared & 0.873041124581175 \tabularnewline
Adjusted R-squared & 0.870889279235094 \tabularnewline
F-TEST (value) & 405.717411881327 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 59 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0376961676966682 \tabularnewline
Sum Squared Residuals & 0.0838390624819047 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58042&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.934366697063404[/C][/ROW]
[ROW][C]R-squared[/C][C]0.873041124581175[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.870889279235094[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]405.717411881327[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]59[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.0376961676966682[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.0838390624819047[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58042&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58042&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 R0.934366697063404
R-squared0.873041124581175
Adjusted R-squared0.870889279235094
F-TEST (value)405.717411881327
F-TEST (DF numerator)1
F-TEST (DF denominator)59
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0376961676966682
Sum Squared Residuals0.0838390624819047







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11.581.65673478487464-0.0767347848746352
21.591.65673478487463-0.0667347848746309
31.61.65673478487463-0.0567347848746306
41.61.65673478487463-0.0567347848746306
51.61.65673478487463-0.0567347848746306
61.61.66497388383809-0.0649738838380912
71.611.66497388383809-0.0549738838380912
81.611.66497388383809-0.0549738838380912
91.621.66497388383809-0.0449738838380911
101.631.66497388383809-0.0349738838380914
111.631.65673478487463-0.0267347848746308
121.631.66497388383809-0.0349738838380914
131.631.65673478487463-0.0267347848746308
141.631.65673478487463-0.0267347848746308
151.641.66497388383809-0.0249738838380913
161.641.65673478487463-0.0167347848746308
171.641.65673478487463-0.0167347848746308
181.651.65673478487463-0.00673478487463078
191.651.65673478487463-0.00673478487463078
201.651.640256586947710.00974341305229031
211.651.640256586947710.00974341305229031
221.651.640256586947710.00974341305229031
231.661.640256586947710.0197434130522903
241.671.648495685911170.0215043140888298
251.681.648495685911170.0315043140888298
261.681.648495685911170.0315043140888298
271.681.656734784874630.0232652151253692
281.681.656734784874630.0232652151253692
291.691.648495685911170.0415043140888298
301.71.656734784874630.0432652151253693
311.71.664973883838090.0350261161619087
321.711.681452081765010.0285479182349877
331.731.689691180728470.0403088192715272
341.731.697930279691930.0320697203080666
351.731.697930279691930.0320697203080666
361.741.697930279691930.0420697203080666
371.741.689691180728470.0503088192715272
381.741.697930279691930.0420697203080666
391.751.697930279691930.0520697203080666
401.781.714408477618850.0655915223811455
411.821.739125774509240.080874225490764
421.831.763843071399620.0661569286003823
431.841.805038566216920.0349614337830796
441.851.846234061034220.00376593896577684
451.861.846234061034220.0137659389657768
461.861.87919045688807-0.0191904568880653
471.871.87919045688807-0.0091904568880653
481.871.87095135792460-0.000951357924604825
491.871.88742955585153-0.0174295558515258
501.871.90390775377845-0.033907753778447
511.871.91214685274191-0.0421468527419075
521.871.90390775377845-0.033907753778447
531.871.90390775377845-0.033907753778447
541.881.879190456888070.000809543111934496
551.881.862712258961140.0172877410388555
561.871.87095135792460-0.000951357924604825
571.871.862712258961140.00728774103885575
581.871.862712258961140.00728774103885575
591.871.862712258961140.00728774103885575
601.871.862712258961140.00728774103885575
611.871.854473159997680.0155268400023163

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.58 & 1.65673478487464 & -0.0767347848746352 \tabularnewline
2 & 1.59 & 1.65673478487463 & -0.0667347848746309 \tabularnewline
3 & 1.6 & 1.65673478487463 & -0.0567347848746306 \tabularnewline
4 & 1.6 & 1.65673478487463 & -0.0567347848746306 \tabularnewline
5 & 1.6 & 1.65673478487463 & -0.0567347848746306 \tabularnewline
6 & 1.6 & 1.66497388383809 & -0.0649738838380912 \tabularnewline
7 & 1.61 & 1.66497388383809 & -0.0549738838380912 \tabularnewline
8 & 1.61 & 1.66497388383809 & -0.0549738838380912 \tabularnewline
9 & 1.62 & 1.66497388383809 & -0.0449738838380911 \tabularnewline
10 & 1.63 & 1.66497388383809 & -0.0349738838380914 \tabularnewline
11 & 1.63 & 1.65673478487463 & -0.0267347848746308 \tabularnewline
12 & 1.63 & 1.66497388383809 & -0.0349738838380914 \tabularnewline
13 & 1.63 & 1.65673478487463 & -0.0267347848746308 \tabularnewline
14 & 1.63 & 1.65673478487463 & -0.0267347848746308 \tabularnewline
15 & 1.64 & 1.66497388383809 & -0.0249738838380913 \tabularnewline
16 & 1.64 & 1.65673478487463 & -0.0167347848746308 \tabularnewline
17 & 1.64 & 1.65673478487463 & -0.0167347848746308 \tabularnewline
18 & 1.65 & 1.65673478487463 & -0.00673478487463078 \tabularnewline
19 & 1.65 & 1.65673478487463 & -0.00673478487463078 \tabularnewline
20 & 1.65 & 1.64025658694771 & 0.00974341305229031 \tabularnewline
21 & 1.65 & 1.64025658694771 & 0.00974341305229031 \tabularnewline
22 & 1.65 & 1.64025658694771 & 0.00974341305229031 \tabularnewline
23 & 1.66 & 1.64025658694771 & 0.0197434130522903 \tabularnewline
24 & 1.67 & 1.64849568591117 & 0.0215043140888298 \tabularnewline
25 & 1.68 & 1.64849568591117 & 0.0315043140888298 \tabularnewline
26 & 1.68 & 1.64849568591117 & 0.0315043140888298 \tabularnewline
27 & 1.68 & 1.65673478487463 & 0.0232652151253692 \tabularnewline
28 & 1.68 & 1.65673478487463 & 0.0232652151253692 \tabularnewline
29 & 1.69 & 1.64849568591117 & 0.0415043140888298 \tabularnewline
30 & 1.7 & 1.65673478487463 & 0.0432652151253693 \tabularnewline
31 & 1.7 & 1.66497388383809 & 0.0350261161619087 \tabularnewline
32 & 1.71 & 1.68145208176501 & 0.0285479182349877 \tabularnewline
33 & 1.73 & 1.68969118072847 & 0.0403088192715272 \tabularnewline
34 & 1.73 & 1.69793027969193 & 0.0320697203080666 \tabularnewline
35 & 1.73 & 1.69793027969193 & 0.0320697203080666 \tabularnewline
36 & 1.74 & 1.69793027969193 & 0.0420697203080666 \tabularnewline
37 & 1.74 & 1.68969118072847 & 0.0503088192715272 \tabularnewline
38 & 1.74 & 1.69793027969193 & 0.0420697203080666 \tabularnewline
39 & 1.75 & 1.69793027969193 & 0.0520697203080666 \tabularnewline
40 & 1.78 & 1.71440847761885 & 0.0655915223811455 \tabularnewline
41 & 1.82 & 1.73912577450924 & 0.080874225490764 \tabularnewline
42 & 1.83 & 1.76384307139962 & 0.0661569286003823 \tabularnewline
43 & 1.84 & 1.80503856621692 & 0.0349614337830796 \tabularnewline
44 & 1.85 & 1.84623406103422 & 0.00376593896577684 \tabularnewline
45 & 1.86 & 1.84623406103422 & 0.0137659389657768 \tabularnewline
46 & 1.86 & 1.87919045688807 & -0.0191904568880653 \tabularnewline
47 & 1.87 & 1.87919045688807 & -0.0091904568880653 \tabularnewline
48 & 1.87 & 1.87095135792460 & -0.000951357924604825 \tabularnewline
49 & 1.87 & 1.88742955585153 & -0.0174295558515258 \tabularnewline
50 & 1.87 & 1.90390775377845 & -0.033907753778447 \tabularnewline
51 & 1.87 & 1.91214685274191 & -0.0421468527419075 \tabularnewline
52 & 1.87 & 1.90390775377845 & -0.033907753778447 \tabularnewline
53 & 1.87 & 1.90390775377845 & -0.033907753778447 \tabularnewline
54 & 1.88 & 1.87919045688807 & 0.000809543111934496 \tabularnewline
55 & 1.88 & 1.86271225896114 & 0.0172877410388555 \tabularnewline
56 & 1.87 & 1.87095135792460 & -0.000951357924604825 \tabularnewline
57 & 1.87 & 1.86271225896114 & 0.00728774103885575 \tabularnewline
58 & 1.87 & 1.86271225896114 & 0.00728774103885575 \tabularnewline
59 & 1.87 & 1.86271225896114 & 0.00728774103885575 \tabularnewline
60 & 1.87 & 1.86271225896114 & 0.00728774103885575 \tabularnewline
61 & 1.87 & 1.85447315999768 & 0.0155268400023163 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58042&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]1.58[/C][C]1.65673478487464[/C][C]-0.0767347848746352[/C][/ROW]
[ROW][C]2[/C][C]1.59[/C][C]1.65673478487463[/C][C]-0.0667347848746309[/C][/ROW]
[ROW][C]3[/C][C]1.6[/C][C]1.65673478487463[/C][C]-0.0567347848746306[/C][/ROW]
[ROW][C]4[/C][C]1.6[/C][C]1.65673478487463[/C][C]-0.0567347848746306[/C][/ROW]
[ROW][C]5[/C][C]1.6[/C][C]1.65673478487463[/C][C]-0.0567347848746306[/C][/ROW]
[ROW][C]6[/C][C]1.6[/C][C]1.66497388383809[/C][C]-0.0649738838380912[/C][/ROW]
[ROW][C]7[/C][C]1.61[/C][C]1.66497388383809[/C][C]-0.0549738838380912[/C][/ROW]
[ROW][C]8[/C][C]1.61[/C][C]1.66497388383809[/C][C]-0.0549738838380912[/C][/ROW]
[ROW][C]9[/C][C]1.62[/C][C]1.66497388383809[/C][C]-0.0449738838380911[/C][/ROW]
[ROW][C]10[/C][C]1.63[/C][C]1.66497388383809[/C][C]-0.0349738838380914[/C][/ROW]
[ROW][C]11[/C][C]1.63[/C][C]1.65673478487463[/C][C]-0.0267347848746308[/C][/ROW]
[ROW][C]12[/C][C]1.63[/C][C]1.66497388383809[/C][C]-0.0349738838380914[/C][/ROW]
[ROW][C]13[/C][C]1.63[/C][C]1.65673478487463[/C][C]-0.0267347848746308[/C][/ROW]
[ROW][C]14[/C][C]1.63[/C][C]1.65673478487463[/C][C]-0.0267347848746308[/C][/ROW]
[ROW][C]15[/C][C]1.64[/C][C]1.66497388383809[/C][C]-0.0249738838380913[/C][/ROW]
[ROW][C]16[/C][C]1.64[/C][C]1.65673478487463[/C][C]-0.0167347848746308[/C][/ROW]
[ROW][C]17[/C][C]1.64[/C][C]1.65673478487463[/C][C]-0.0167347848746308[/C][/ROW]
[ROW][C]18[/C][C]1.65[/C][C]1.65673478487463[/C][C]-0.00673478487463078[/C][/ROW]
[ROW][C]19[/C][C]1.65[/C][C]1.65673478487463[/C][C]-0.00673478487463078[/C][/ROW]
[ROW][C]20[/C][C]1.65[/C][C]1.64025658694771[/C][C]0.00974341305229031[/C][/ROW]
[ROW][C]21[/C][C]1.65[/C][C]1.64025658694771[/C][C]0.00974341305229031[/C][/ROW]
[ROW][C]22[/C][C]1.65[/C][C]1.64025658694771[/C][C]0.00974341305229031[/C][/ROW]
[ROW][C]23[/C][C]1.66[/C][C]1.64025658694771[/C][C]0.0197434130522903[/C][/ROW]
[ROW][C]24[/C][C]1.67[/C][C]1.64849568591117[/C][C]0.0215043140888298[/C][/ROW]
[ROW][C]25[/C][C]1.68[/C][C]1.64849568591117[/C][C]0.0315043140888298[/C][/ROW]
[ROW][C]26[/C][C]1.68[/C][C]1.64849568591117[/C][C]0.0315043140888298[/C][/ROW]
[ROW][C]27[/C][C]1.68[/C][C]1.65673478487463[/C][C]0.0232652151253692[/C][/ROW]
[ROW][C]28[/C][C]1.68[/C][C]1.65673478487463[/C][C]0.0232652151253692[/C][/ROW]
[ROW][C]29[/C][C]1.69[/C][C]1.64849568591117[/C][C]0.0415043140888298[/C][/ROW]
[ROW][C]30[/C][C]1.7[/C][C]1.65673478487463[/C][C]0.0432652151253693[/C][/ROW]
[ROW][C]31[/C][C]1.7[/C][C]1.66497388383809[/C][C]0.0350261161619087[/C][/ROW]
[ROW][C]32[/C][C]1.71[/C][C]1.68145208176501[/C][C]0.0285479182349877[/C][/ROW]
[ROW][C]33[/C][C]1.73[/C][C]1.68969118072847[/C][C]0.0403088192715272[/C][/ROW]
[ROW][C]34[/C][C]1.73[/C][C]1.69793027969193[/C][C]0.0320697203080666[/C][/ROW]
[ROW][C]35[/C][C]1.73[/C][C]1.69793027969193[/C][C]0.0320697203080666[/C][/ROW]
[ROW][C]36[/C][C]1.74[/C][C]1.69793027969193[/C][C]0.0420697203080666[/C][/ROW]
[ROW][C]37[/C][C]1.74[/C][C]1.68969118072847[/C][C]0.0503088192715272[/C][/ROW]
[ROW][C]38[/C][C]1.74[/C][C]1.69793027969193[/C][C]0.0420697203080666[/C][/ROW]
[ROW][C]39[/C][C]1.75[/C][C]1.69793027969193[/C][C]0.0520697203080666[/C][/ROW]
[ROW][C]40[/C][C]1.78[/C][C]1.71440847761885[/C][C]0.0655915223811455[/C][/ROW]
[ROW][C]41[/C][C]1.82[/C][C]1.73912577450924[/C][C]0.080874225490764[/C][/ROW]
[ROW][C]42[/C][C]1.83[/C][C]1.76384307139962[/C][C]0.0661569286003823[/C][/ROW]
[ROW][C]43[/C][C]1.84[/C][C]1.80503856621692[/C][C]0.0349614337830796[/C][/ROW]
[ROW][C]44[/C][C]1.85[/C][C]1.84623406103422[/C][C]0.00376593896577684[/C][/ROW]
[ROW][C]45[/C][C]1.86[/C][C]1.84623406103422[/C][C]0.0137659389657768[/C][/ROW]
[ROW][C]46[/C][C]1.86[/C][C]1.87919045688807[/C][C]-0.0191904568880653[/C][/ROW]
[ROW][C]47[/C][C]1.87[/C][C]1.87919045688807[/C][C]-0.0091904568880653[/C][/ROW]
[ROW][C]48[/C][C]1.87[/C][C]1.87095135792460[/C][C]-0.000951357924604825[/C][/ROW]
[ROW][C]49[/C][C]1.87[/C][C]1.88742955585153[/C][C]-0.0174295558515258[/C][/ROW]
[ROW][C]50[/C][C]1.87[/C][C]1.90390775377845[/C][C]-0.033907753778447[/C][/ROW]
[ROW][C]51[/C][C]1.87[/C][C]1.91214685274191[/C][C]-0.0421468527419075[/C][/ROW]
[ROW][C]52[/C][C]1.87[/C][C]1.90390775377845[/C][C]-0.033907753778447[/C][/ROW]
[ROW][C]53[/C][C]1.87[/C][C]1.90390775377845[/C][C]-0.033907753778447[/C][/ROW]
[ROW][C]54[/C][C]1.88[/C][C]1.87919045688807[/C][C]0.000809543111934496[/C][/ROW]
[ROW][C]55[/C][C]1.88[/C][C]1.86271225896114[/C][C]0.0172877410388555[/C][/ROW]
[ROW][C]56[/C][C]1.87[/C][C]1.87095135792460[/C][C]-0.000951357924604825[/C][/ROW]
[ROW][C]57[/C][C]1.87[/C][C]1.86271225896114[/C][C]0.00728774103885575[/C][/ROW]
[ROW][C]58[/C][C]1.87[/C][C]1.86271225896114[/C][C]0.00728774103885575[/C][/ROW]
[ROW][C]59[/C][C]1.87[/C][C]1.86271225896114[/C][C]0.00728774103885575[/C][/ROW]
[ROW][C]60[/C][C]1.87[/C][C]1.86271225896114[/C][C]0.00728774103885575[/C][/ROW]
[ROW][C]61[/C][C]1.87[/C][C]1.85447315999768[/C][C]0.0155268400023163[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58042&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58042&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
11.581.65673478487464-0.0767347848746352
21.591.65673478487463-0.0667347848746309
31.61.65673478487463-0.0567347848746306
41.61.65673478487463-0.0567347848746306
51.61.65673478487463-0.0567347848746306
61.61.66497388383809-0.0649738838380912
71.611.66497388383809-0.0549738838380912
81.611.66497388383809-0.0549738838380912
91.621.66497388383809-0.0449738838380911
101.631.66497388383809-0.0349738838380914
111.631.65673478487463-0.0267347848746308
121.631.66497388383809-0.0349738838380914
131.631.65673478487463-0.0267347848746308
141.631.65673478487463-0.0267347848746308
151.641.66497388383809-0.0249738838380913
161.641.65673478487463-0.0167347848746308
171.641.65673478487463-0.0167347848746308
181.651.65673478487463-0.00673478487463078
191.651.65673478487463-0.00673478487463078
201.651.640256586947710.00974341305229031
211.651.640256586947710.00974341305229031
221.651.640256586947710.00974341305229031
231.661.640256586947710.0197434130522903
241.671.648495685911170.0215043140888298
251.681.648495685911170.0315043140888298
261.681.648495685911170.0315043140888298
271.681.656734784874630.0232652151253692
281.681.656734784874630.0232652151253692
291.691.648495685911170.0415043140888298
301.71.656734784874630.0432652151253693
311.71.664973883838090.0350261161619087
321.711.681452081765010.0285479182349877
331.731.689691180728470.0403088192715272
341.731.697930279691930.0320697203080666
351.731.697930279691930.0320697203080666
361.741.697930279691930.0420697203080666
371.741.689691180728470.0503088192715272
381.741.697930279691930.0420697203080666
391.751.697930279691930.0520697203080666
401.781.714408477618850.0655915223811455
411.821.739125774509240.080874225490764
421.831.763843071399620.0661569286003823
431.841.805038566216920.0349614337830796
441.851.846234061034220.00376593896577684
451.861.846234061034220.0137659389657768
461.861.87919045688807-0.0191904568880653
471.871.87919045688807-0.0091904568880653
481.871.87095135792460-0.000951357924604825
491.871.88742955585153-0.0174295558515258
501.871.90390775377845-0.033907753778447
511.871.91214685274191-0.0421468527419075
521.871.90390775377845-0.033907753778447
531.871.90390775377845-0.033907753778447
541.881.879190456888070.000809543111934496
551.881.862712258961140.0172877410388555
561.871.87095135792460-0.000951357924604825
571.871.862712258961140.00728774103885575
581.871.862712258961140.00728774103885575
591.871.862712258961140.00728774103885575
601.871.862712258961140.00728774103885575
611.871.854473159997680.0155268400023163







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.03730985775627830.07461971551255650.962690142243722
60.01096670627015860.02193341254031720.989033293729841
70.004364618411125890.008729236822251770.995635381588874
80.001544711778858390.003089423557716780.998455288221142
90.001259359631697590.002518719263395180.998740640368302
100.002430070651990960.004860141303981920.99756992934801
110.02158866393761870.04317732787523730.978411336062381
120.02091848109603500.04183696219206990.979081518903965
130.04524080456433610.09048160912867230.954759195435664
140.07059797847369730.1411959569473950.929402021526303
150.09763782437462220.1952756487492440.902362175625378
160.1783949791617770.3567899583235530.821605020838223
170.271448723402340.542897446804680.72855127659766
180.4188652311421820.8377304622843640.581134768857818
190.5578846163710580.8842307672578840.442115383628942
200.580891526550730.838216946898540.41910847344927
210.5843405397195590.8313189205608810.415659460280441
220.600199653606730.799600692786540.39980034639327
230.6069949690078610.7860100619842790.393005030992139
240.7001403297251250.5997193405497510.299859670274875
250.7897751795762040.4204496408475930.210224820423796
260.8441237318950510.3117525362098980.155876268104949
270.932347464722150.1353050705557010.0676525352778506
280.973306167165850.0533876656683010.0266938328341505
290.9810255431553360.03794891368932720.0189744568446636
300.9931556471061220.01368870578775650.00684435289387826
310.9986115263776960.002776947244608230.00138847362230411
320.999863721145510.0002725577089795290.000136278854489765
330.9999550679761088.9864047784517e-054.49320238922585e-05
340.999974455179495.10896410206127e-052.55448205103063e-05
350.9999865813922862.68372154272965e-051.34186077136483e-05
360.9999896396252382.07207495246542e-051.03603747623271e-05
370.9999929140495171.41719009667598e-057.08595048337989e-06
380.9999989898936542.02021269216572e-061.01010634608286e-06
390.9999999816532163.66935686114807e-081.83467843057403e-08
400.9999999996407547.18490896025335e-103.59245448012667e-10
410.9999999987879592.42408250501001e-091.21204125250500e-09
420.9999999969770526.04589568283019e-093.02294784141510e-09
430.9999999992259471.5481066279839e-097.7405331399195e-10
440.9999999999674466.51081611684099e-113.25540805842050e-11
450.9999999999761194.77624147245039e-112.38812073622519e-11
460.9999999999955848.831264032374e-124.415632016187e-12
470.9999999999586548.26929746931182e-114.13464873465591e-11
480.999999999600357.99298770669757e-103.99649385334879e-10
490.9999999962915067.41698701143813e-093.70849350571907e-09
500.9999999673930596.52138825249516e-083.26069412624758e-08
510.999999723010375.53979261390577e-072.76989630695289e-07
520.9999977630634974.47387300538111e-062.23693650269056e-06
530.9999931150920831.37698158349266e-056.88490791746328e-06
540.999962474986947.50500261204738e-053.75250130602369e-05
5518.27832263769462e-564.13916131884731e-56
56100

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.0373098577562783 & 0.0746197155125565 & 0.962690142243722 \tabularnewline
6 & 0.0109667062701586 & 0.0219334125403172 & 0.989033293729841 \tabularnewline
7 & 0.00436461841112589 & 0.00872923682225177 & 0.995635381588874 \tabularnewline
8 & 0.00154471177885839 & 0.00308942355771678 & 0.998455288221142 \tabularnewline
9 & 0.00125935963169759 & 0.00251871926339518 & 0.998740640368302 \tabularnewline
10 & 0.00243007065199096 & 0.00486014130398192 & 0.99756992934801 \tabularnewline
11 & 0.0215886639376187 & 0.0431773278752373 & 0.978411336062381 \tabularnewline
12 & 0.0209184810960350 & 0.0418369621920699 & 0.979081518903965 \tabularnewline
13 & 0.0452408045643361 & 0.0904816091286723 & 0.954759195435664 \tabularnewline
14 & 0.0705979784736973 & 0.141195956947395 & 0.929402021526303 \tabularnewline
15 & 0.0976378243746222 & 0.195275648749244 & 0.902362175625378 \tabularnewline
16 & 0.178394979161777 & 0.356789958323553 & 0.821605020838223 \tabularnewline
17 & 0.27144872340234 & 0.54289744680468 & 0.72855127659766 \tabularnewline
18 & 0.418865231142182 & 0.837730462284364 & 0.581134768857818 \tabularnewline
19 & 0.557884616371058 & 0.884230767257884 & 0.442115383628942 \tabularnewline
20 & 0.58089152655073 & 0.83821694689854 & 0.41910847344927 \tabularnewline
21 & 0.584340539719559 & 0.831318920560881 & 0.415659460280441 \tabularnewline
22 & 0.60019965360673 & 0.79960069278654 & 0.39980034639327 \tabularnewline
23 & 0.606994969007861 & 0.786010061984279 & 0.393005030992139 \tabularnewline
24 & 0.700140329725125 & 0.599719340549751 & 0.299859670274875 \tabularnewline
25 & 0.789775179576204 & 0.420449640847593 & 0.210224820423796 \tabularnewline
26 & 0.844123731895051 & 0.311752536209898 & 0.155876268104949 \tabularnewline
27 & 0.93234746472215 & 0.135305070555701 & 0.0676525352778506 \tabularnewline
28 & 0.97330616716585 & 0.053387665668301 & 0.0266938328341505 \tabularnewline
29 & 0.981025543155336 & 0.0379489136893272 & 0.0189744568446636 \tabularnewline
30 & 0.993155647106122 & 0.0136887057877565 & 0.00684435289387826 \tabularnewline
31 & 0.998611526377696 & 0.00277694724460823 & 0.00138847362230411 \tabularnewline
32 & 0.99986372114551 & 0.000272557708979529 & 0.000136278854489765 \tabularnewline
33 & 0.999955067976108 & 8.9864047784517e-05 & 4.49320238922585e-05 \tabularnewline
34 & 0.99997445517949 & 5.10896410206127e-05 & 2.55448205103063e-05 \tabularnewline
35 & 0.999986581392286 & 2.68372154272965e-05 & 1.34186077136483e-05 \tabularnewline
36 & 0.999989639625238 & 2.07207495246542e-05 & 1.03603747623271e-05 \tabularnewline
37 & 0.999992914049517 & 1.41719009667598e-05 & 7.08595048337989e-06 \tabularnewline
38 & 0.999998989893654 & 2.02021269216572e-06 & 1.01010634608286e-06 \tabularnewline
39 & 0.999999981653216 & 3.66935686114807e-08 & 1.83467843057403e-08 \tabularnewline
40 & 0.999999999640754 & 7.18490896025335e-10 & 3.59245448012667e-10 \tabularnewline
41 & 0.999999998787959 & 2.42408250501001e-09 & 1.21204125250500e-09 \tabularnewline
42 & 0.999999996977052 & 6.04589568283019e-09 & 3.02294784141510e-09 \tabularnewline
43 & 0.999999999225947 & 1.5481066279839e-09 & 7.7405331399195e-10 \tabularnewline
44 & 0.999999999967446 & 6.51081611684099e-11 & 3.25540805842050e-11 \tabularnewline
45 & 0.999999999976119 & 4.77624147245039e-11 & 2.38812073622519e-11 \tabularnewline
46 & 0.999999999995584 & 8.831264032374e-12 & 4.415632016187e-12 \tabularnewline
47 & 0.999999999958654 & 8.26929746931182e-11 & 4.13464873465591e-11 \tabularnewline
48 & 0.99999999960035 & 7.99298770669757e-10 & 3.99649385334879e-10 \tabularnewline
49 & 0.999999996291506 & 7.41698701143813e-09 & 3.70849350571907e-09 \tabularnewline
50 & 0.999999967393059 & 6.52138825249516e-08 & 3.26069412624758e-08 \tabularnewline
51 & 0.99999972301037 & 5.53979261390577e-07 & 2.76989630695289e-07 \tabularnewline
52 & 0.999997763063497 & 4.47387300538111e-06 & 2.23693650269056e-06 \tabularnewline
53 & 0.999993115092083 & 1.37698158349266e-05 & 6.88490791746328e-06 \tabularnewline
54 & 0.99996247498694 & 7.50500261204738e-05 & 3.75250130602369e-05 \tabularnewline
55 & 1 & 8.27832263769462e-56 & 4.13916131884731e-56 \tabularnewline
56 & 1 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58042&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]0.0373098577562783[/C][C]0.0746197155125565[/C][C]0.962690142243722[/C][/ROW]
[ROW][C]6[/C][C]0.0109667062701586[/C][C]0.0219334125403172[/C][C]0.989033293729841[/C][/ROW]
[ROW][C]7[/C][C]0.00436461841112589[/C][C]0.00872923682225177[/C][C]0.995635381588874[/C][/ROW]
[ROW][C]8[/C][C]0.00154471177885839[/C][C]0.00308942355771678[/C][C]0.998455288221142[/C][/ROW]
[ROW][C]9[/C][C]0.00125935963169759[/C][C]0.00251871926339518[/C][C]0.998740640368302[/C][/ROW]
[ROW][C]10[/C][C]0.00243007065199096[/C][C]0.00486014130398192[/C][C]0.99756992934801[/C][/ROW]
[ROW][C]11[/C][C]0.0215886639376187[/C][C]0.0431773278752373[/C][C]0.978411336062381[/C][/ROW]
[ROW][C]12[/C][C]0.0209184810960350[/C][C]0.0418369621920699[/C][C]0.979081518903965[/C][/ROW]
[ROW][C]13[/C][C]0.0452408045643361[/C][C]0.0904816091286723[/C][C]0.954759195435664[/C][/ROW]
[ROW][C]14[/C][C]0.0705979784736973[/C][C]0.141195956947395[/C][C]0.929402021526303[/C][/ROW]
[ROW][C]15[/C][C]0.0976378243746222[/C][C]0.195275648749244[/C][C]0.902362175625378[/C][/ROW]
[ROW][C]16[/C][C]0.178394979161777[/C][C]0.356789958323553[/C][C]0.821605020838223[/C][/ROW]
[ROW][C]17[/C][C]0.27144872340234[/C][C]0.54289744680468[/C][C]0.72855127659766[/C][/ROW]
[ROW][C]18[/C][C]0.418865231142182[/C][C]0.837730462284364[/C][C]0.581134768857818[/C][/ROW]
[ROW][C]19[/C][C]0.557884616371058[/C][C]0.884230767257884[/C][C]0.442115383628942[/C][/ROW]
[ROW][C]20[/C][C]0.58089152655073[/C][C]0.83821694689854[/C][C]0.41910847344927[/C][/ROW]
[ROW][C]21[/C][C]0.584340539719559[/C][C]0.831318920560881[/C][C]0.415659460280441[/C][/ROW]
[ROW][C]22[/C][C]0.60019965360673[/C][C]0.79960069278654[/C][C]0.39980034639327[/C][/ROW]
[ROW][C]23[/C][C]0.606994969007861[/C][C]0.786010061984279[/C][C]0.393005030992139[/C][/ROW]
[ROW][C]24[/C][C]0.700140329725125[/C][C]0.599719340549751[/C][C]0.299859670274875[/C][/ROW]
[ROW][C]25[/C][C]0.789775179576204[/C][C]0.420449640847593[/C][C]0.210224820423796[/C][/ROW]
[ROW][C]26[/C][C]0.844123731895051[/C][C]0.311752536209898[/C][C]0.155876268104949[/C][/ROW]
[ROW][C]27[/C][C]0.93234746472215[/C][C]0.135305070555701[/C][C]0.0676525352778506[/C][/ROW]
[ROW][C]28[/C][C]0.97330616716585[/C][C]0.053387665668301[/C][C]0.0266938328341505[/C][/ROW]
[ROW][C]29[/C][C]0.981025543155336[/C][C]0.0379489136893272[/C][C]0.0189744568446636[/C][/ROW]
[ROW][C]30[/C][C]0.993155647106122[/C][C]0.0136887057877565[/C][C]0.00684435289387826[/C][/ROW]
[ROW][C]31[/C][C]0.998611526377696[/C][C]0.00277694724460823[/C][C]0.00138847362230411[/C][/ROW]
[ROW][C]32[/C][C]0.99986372114551[/C][C]0.000272557708979529[/C][C]0.000136278854489765[/C][/ROW]
[ROW][C]33[/C][C]0.999955067976108[/C][C]8.9864047784517e-05[/C][C]4.49320238922585e-05[/C][/ROW]
[ROW][C]34[/C][C]0.99997445517949[/C][C]5.10896410206127e-05[/C][C]2.55448205103063e-05[/C][/ROW]
[ROW][C]35[/C][C]0.999986581392286[/C][C]2.68372154272965e-05[/C][C]1.34186077136483e-05[/C][/ROW]
[ROW][C]36[/C][C]0.999989639625238[/C][C]2.07207495246542e-05[/C][C]1.03603747623271e-05[/C][/ROW]
[ROW][C]37[/C][C]0.999992914049517[/C][C]1.41719009667598e-05[/C][C]7.08595048337989e-06[/C][/ROW]
[ROW][C]38[/C][C]0.999998989893654[/C][C]2.02021269216572e-06[/C][C]1.01010634608286e-06[/C][/ROW]
[ROW][C]39[/C][C]0.999999981653216[/C][C]3.66935686114807e-08[/C][C]1.83467843057403e-08[/C][/ROW]
[ROW][C]40[/C][C]0.999999999640754[/C][C]7.18490896025335e-10[/C][C]3.59245448012667e-10[/C][/ROW]
[ROW][C]41[/C][C]0.999999998787959[/C][C]2.42408250501001e-09[/C][C]1.21204125250500e-09[/C][/ROW]
[ROW][C]42[/C][C]0.999999996977052[/C][C]6.04589568283019e-09[/C][C]3.02294784141510e-09[/C][/ROW]
[ROW][C]43[/C][C]0.999999999225947[/C][C]1.5481066279839e-09[/C][C]7.7405331399195e-10[/C][/ROW]
[ROW][C]44[/C][C]0.999999999967446[/C][C]6.51081611684099e-11[/C][C]3.25540805842050e-11[/C][/ROW]
[ROW][C]45[/C][C]0.999999999976119[/C][C]4.77624147245039e-11[/C][C]2.38812073622519e-11[/C][/ROW]
[ROW][C]46[/C][C]0.999999999995584[/C][C]8.831264032374e-12[/C][C]4.415632016187e-12[/C][/ROW]
[ROW][C]47[/C][C]0.999999999958654[/C][C]8.26929746931182e-11[/C][C]4.13464873465591e-11[/C][/ROW]
[ROW][C]48[/C][C]0.99999999960035[/C][C]7.99298770669757e-10[/C][C]3.99649385334879e-10[/C][/ROW]
[ROW][C]49[/C][C]0.999999996291506[/C][C]7.41698701143813e-09[/C][C]3.70849350571907e-09[/C][/ROW]
[ROW][C]50[/C][C]0.999999967393059[/C][C]6.52138825249516e-08[/C][C]3.26069412624758e-08[/C][/ROW]
[ROW][C]51[/C][C]0.99999972301037[/C][C]5.53979261390577e-07[/C][C]2.76989630695289e-07[/C][/ROW]
[ROW][C]52[/C][C]0.999997763063497[/C][C]4.47387300538111e-06[/C][C]2.23693650269056e-06[/C][/ROW]
[ROW][C]53[/C][C]0.999993115092083[/C][C]1.37698158349266e-05[/C][C]6.88490791746328e-06[/C][/ROW]
[ROW][C]54[/C][C]0.99996247498694[/C][C]7.50500261204738e-05[/C][C]3.75250130602369e-05[/C][/ROW]
[ROW][C]55[/C][C]1[/C][C]8.27832263769462e-56[/C][C]4.13916131884731e-56[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58042&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58042&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
50.03730985775627830.07461971551255650.962690142243722
60.01096670627015860.02193341254031720.989033293729841
70.004364618411125890.008729236822251770.995635381588874
80.001544711778858390.003089423557716780.998455288221142
90.001259359631697590.002518719263395180.998740640368302
100.002430070651990960.004860141303981920.99756992934801
110.02158866393761870.04317732787523730.978411336062381
120.02091848109603500.04183696219206990.979081518903965
130.04524080456433610.09048160912867230.954759195435664
140.07059797847369730.1411959569473950.929402021526303
150.09763782437462220.1952756487492440.902362175625378
160.1783949791617770.3567899583235530.821605020838223
170.271448723402340.542897446804680.72855127659766
180.4188652311421820.8377304622843640.581134768857818
190.5578846163710580.8842307672578840.442115383628942
200.580891526550730.838216946898540.41910847344927
210.5843405397195590.8313189205608810.415659460280441
220.600199653606730.799600692786540.39980034639327
230.6069949690078610.7860100619842790.393005030992139
240.7001403297251250.5997193405497510.299859670274875
250.7897751795762040.4204496408475930.210224820423796
260.8441237318950510.3117525362098980.155876268104949
270.932347464722150.1353050705557010.0676525352778506
280.973306167165850.0533876656683010.0266938328341505
290.9810255431553360.03794891368932720.0189744568446636
300.9931556471061220.01368870578775650.00684435289387826
310.9986115263776960.002776947244608230.00138847362230411
320.999863721145510.0002725577089795290.000136278854489765
330.9999550679761088.9864047784517e-054.49320238922585e-05
340.999974455179495.10896410206127e-052.55448205103063e-05
350.9999865813922862.68372154272965e-051.34186077136483e-05
360.9999896396252382.07207495246542e-051.03603747623271e-05
370.9999929140495171.41719009667598e-057.08595048337989e-06
380.9999989898936542.02021269216572e-061.01010634608286e-06
390.9999999816532163.66935686114807e-081.83467843057403e-08
400.9999999996407547.18490896025335e-103.59245448012667e-10
410.9999999987879592.42408250501001e-091.21204125250500e-09
420.9999999969770526.04589568283019e-093.02294784141510e-09
430.9999999992259471.5481066279839e-097.7405331399195e-10
440.9999999999674466.51081611684099e-113.25540805842050e-11
450.9999999999761194.77624147245039e-112.38812073622519e-11
460.9999999999955848.831264032374e-124.415632016187e-12
470.9999999999586548.26929746931182e-114.13464873465591e-11
480.999999999600357.99298770669757e-103.99649385334879e-10
490.9999999962915067.41698701143813e-093.70849350571907e-09
500.9999999673930596.52138825249516e-083.26069412624758e-08
510.999999723010375.53979261390577e-072.76989630695289e-07
520.9999977630634974.47387300538111e-062.23693650269056e-06
530.9999931150920831.37698158349266e-056.88490791746328e-06
540.999962474986947.50500261204738e-053.75250130602369e-05
5518.27832263769462e-564.13916131884731e-56
56100







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level300.576923076923077NOK
5% type I error level350.673076923076923NOK
10% type I error level380.730769230769231NOK

\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 & 30 & 0.576923076923077 & NOK \tabularnewline
5% type I error level & 35 & 0.673076923076923 & NOK \tabularnewline
10% type I error level & 38 & 0.730769230769231 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58042&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]30[/C][C]0.576923076923077[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]35[/C][C]0.673076923076923[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]38[/C][C]0.730769230769231[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58042&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58042&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 level300.576923076923077NOK
5% type I error level350.673076923076923NOK
10% type I error level380.730769230769231NOK



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')
}