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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 04:49:55 -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/t1258717857fljc0skms2mbege.htm/, Retrieved Fri, 29 Mar 2024 07:34:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58047, Retrieved Fri, 29 Mar 2024 07:34:46 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact182
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:06:21] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [link 3] [2009-11-20 11:49:55] [454b2df2fae01897bad5ff38ed3cc924] [Current]
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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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58047&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58047&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58047&T=0

As an alternative you can also use a QR Code:  

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

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







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 1.39450175395646 + 0.340169624901634X[t] -0.00181850174385351M1[t] -0.00116977140470005M2[t] -0.000997065664072645M3[t] + 0.00121665782596459M4[t] + 0.00606970281639524M5[t] + 0.00892274780682585M6[t] + 0.00641511429764998M7[t] + 0.000546802288867615M8[t] + 0.00476052577890482M9[t] -0.000427446980074374M10[t] + 0.00046661575976609M11[t] + 0.00378627650996281t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  1.39450175395646 +  0.340169624901634X[t] -0.00181850174385351M1[t] -0.00116977140470005M2[t] -0.000997065664072645M3[t] +  0.00121665782596459M4[t] +  0.00606970281639524M5[t] +  0.00892274780682585M6[t] +  0.00641511429764998M7[t] +  0.000546802288867615M8[t] +  0.00476052577890482M9[t] -0.000427446980074374M10[t] +  0.00046661575976609M11[t] +  0.00378627650996281t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58047&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  1.39450175395646 +  0.340169624901634X[t] -0.00181850174385351M1[t] -0.00116977140470005M2[t] -0.000997065664072645M3[t] +  0.00121665782596459M4[t] +  0.00606970281639524M5[t] +  0.00892274780682585M6[t] +  0.00641511429764998M7[t] +  0.000546802288867615M8[t] +  0.00476052577890482M9[t] -0.000427446980074374M10[t] +  0.00046661575976609M11[t] +  0.00378627650996281t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58047&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58047&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] = + 1.39450175395646 + 0.340169624901634X[t] -0.00181850174385351M1[t] -0.00116977140470005M2[t] -0.000997065664072645M3[t] + 0.00121665782596459M4[t] + 0.00606970281639524M5[t] + 0.00892274780682585M6[t] + 0.00641511429764998M7[t] + 0.000546802288867615M8[t] + 0.00476052577890482M9[t] -0.000427446980074374M10[t] + 0.00046661575976609M11[t] + 0.00378627650996281t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.394501753956460.01664483.783200
X0.3401696249016340.03220410.562900
M1-0.001818501743853510.009256-0.19650.8450880.422544
M2-0.001169771404700050.009721-0.12030.9047280.452364
M3-0.0009970656640726450.009709-0.10270.9186390.459319
M40.001216657825964590.0096910.12550.9006240.450312
M50.006069702816395240.009680.62710.533650.266825
M60.008922747806825850.009670.92270.3608620.180431
M70.006415114297649980.0096640.66380.5100570.255029
M80.0005468022888676150.0096650.05660.9551230.477561
M90.004760525778904820.0096540.49310.6242280.312114
M10-0.0004274469800743740.009655-0.04430.9648750.482438
M110.000466615759766090.0096470.04840.9616260.480813
t0.003786276509962810.00021717.43300

\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) & 1.39450175395646 & 0.016644 & 83.7832 & 0 & 0 \tabularnewline
X & 0.340169624901634 & 0.032204 & 10.5629 & 0 & 0 \tabularnewline
M1 & -0.00181850174385351 & 0.009256 & -0.1965 & 0.845088 & 0.422544 \tabularnewline
M2 & -0.00116977140470005 & 0.009721 & -0.1203 & 0.904728 & 0.452364 \tabularnewline
M3 & -0.000997065664072645 & 0.009709 & -0.1027 & 0.918639 & 0.459319 \tabularnewline
M4 & 0.00121665782596459 & 0.009691 & 0.1255 & 0.900624 & 0.450312 \tabularnewline
M5 & 0.00606970281639524 & 0.00968 & 0.6271 & 0.53365 & 0.266825 \tabularnewline
M6 & 0.00892274780682585 & 0.00967 & 0.9227 & 0.360862 & 0.180431 \tabularnewline
M7 & 0.00641511429764998 & 0.009664 & 0.6638 & 0.510057 & 0.255029 \tabularnewline
M8 & 0.000546802288867615 & 0.009665 & 0.0566 & 0.955123 & 0.477561 \tabularnewline
M9 & 0.00476052577890482 & 0.009654 & 0.4931 & 0.624228 & 0.312114 \tabularnewline
M10 & -0.000427446980074374 & 0.009655 & -0.0443 & 0.964875 & 0.482438 \tabularnewline
M11 & 0.00046661575976609 & 0.009647 & 0.0484 & 0.961626 & 0.480813 \tabularnewline
t & 0.00378627650996281 & 0.000217 & 17.433 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58047&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]1.39450175395646[/C][C]0.016644[/C][C]83.7832[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]0.340169624901634[/C][C]0.032204[/C][C]10.5629[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-0.00181850174385351[/C][C]0.009256[/C][C]-0.1965[/C][C]0.845088[/C][C]0.422544[/C][/ROW]
[ROW][C]M2[/C][C]-0.00116977140470005[/C][C]0.009721[/C][C]-0.1203[/C][C]0.904728[/C][C]0.452364[/C][/ROW]
[ROW][C]M3[/C][C]-0.000997065664072645[/C][C]0.009709[/C][C]-0.1027[/C][C]0.918639[/C][C]0.459319[/C][/ROW]
[ROW][C]M4[/C][C]0.00121665782596459[/C][C]0.009691[/C][C]0.1255[/C][C]0.900624[/C][C]0.450312[/C][/ROW]
[ROW][C]M5[/C][C]0.00606970281639524[/C][C]0.00968[/C][C]0.6271[/C][C]0.53365[/C][C]0.266825[/C][/ROW]
[ROW][C]M6[/C][C]0.00892274780682585[/C][C]0.00967[/C][C]0.9227[/C][C]0.360862[/C][C]0.180431[/C][/ROW]
[ROW][C]M7[/C][C]0.00641511429764998[/C][C]0.009664[/C][C]0.6638[/C][C]0.510057[/C][C]0.255029[/C][/ROW]
[ROW][C]M8[/C][C]0.000546802288867615[/C][C]0.009665[/C][C]0.0566[/C][C]0.955123[/C][C]0.477561[/C][/ROW]
[ROW][C]M9[/C][C]0.00476052577890482[/C][C]0.009654[/C][C]0.4931[/C][C]0.624228[/C][C]0.312114[/C][/ROW]
[ROW][C]M10[/C][C]-0.000427446980074374[/C][C]0.009655[/C][C]-0.0443[/C][C]0.964875[/C][C]0.482438[/C][/ROW]
[ROW][C]M11[/C][C]0.00046661575976609[/C][C]0.009647[/C][C]0.0484[/C][C]0.961626[/C][C]0.480813[/C][/ROW]
[ROW][C]t[/C][C]0.00378627650996281[/C][C]0.000217[/C][C]17.433[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58047&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58047&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)1.394501753956460.01664483.783200
X0.3401696249016340.03220410.562900
M1-0.001818501743853510.009256-0.19650.8450880.422544
M2-0.001169771404700050.009721-0.12030.9047280.452364
M3-0.0009970656640726450.009709-0.10270.9186390.459319
M40.001216657825964590.0096910.12550.9006240.450312
M50.006069702816395240.009680.62710.533650.266825
M60.008922747806825850.009670.92270.3608620.180431
M70.006415114297649980.0096640.66380.5100570.255029
M80.0005468022888676150.0096650.05660.9551230.477561
M90.004760525778904820.0096540.49310.6242280.312114
M10-0.0004274469800743740.009655-0.04430.9648750.482438
M110.000466615759766090.0096470.04840.9616260.480813
t0.003786276509962810.00021717.43300







Multiple Linear Regression - Regression Statistics
Multiple R0.991688932549206
R-squared0.983446938940583
Adjusted R-squared0.978868432690106
F-TEST (value)214.796460928308
F-TEST (DF numerator)13
F-TEST (DF denominator)47
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0152504234527843
Sum Squared Residuals0.0109310445279940

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.991688932549206 \tabularnewline
R-squared & 0.983446938940583 \tabularnewline
Adjusted R-squared & 0.978868432690106 \tabularnewline
F-TEST (value) & 214.796460928308 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 47 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0152504234527843 \tabularnewline
Sum Squared Residuals & 0.0109310445279940 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58047&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.991688932549206[/C][/ROW]
[ROW][C]R-squared[/C][C]0.983446938940583[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.978868432690106[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]214.796460928308[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]47[/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]0.0152504234527843[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.0109310445279940[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58047&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58047&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 R0.991688932549206
R-squared0.983446938940583
Adjusted R-squared0.978868432690106
F-TEST (value)214.796460928308
F-TEST (DF numerator)13
F-TEST (DF denominator)47
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0152504234527843
Sum Squared Residuals0.0109310445279940







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11.581.58356282241847-0.00356282241847002
21.591.587997829267580.00200217073241852
31.61.591956811518170.00804318848182836
41.61.597956811518170.00204318848182837
51.61.60659613301857-0.00659613301856506
61.61.61663715076798-0.0166371507679749
71.611.61791579376876-0.00791579376876178
81.611.61583375826994-0.00583375826994225
91.621.62383375826994-0.00383375826994223
101.631.622432062020930.00756793797907393
111.631.623710705021710.006289294978287
121.631.63043206202093-0.000432062020926052
131.631.628998140538020.00100185946198101
141.631.63343314738714-0.00343314738713537
151.641.64079382588674-0.000793825886741844
161.641.64339212963773-0.0033921296377255
171.641.65203145113812-0.0120314511381190
181.651.65867077263851-0.00867077263851238
191.651.6599494156393-0.00994941563929933
201.651.65106398764245-0.00106398764244708
211.651.65906398764245-0.0090639876424471
221.651.65766229139343-0.00766229139343073
231.661.66234263064323-0.00234263064323399
241.671.669063987642450.000936012357552973
251.681.671031762408560.0089682375914437
261.681.675466769257670.00453323074232744
271.681.68282744775728-0.00282744775727914
281.681.68882744775728-0.00882744775727915
291.691.69406507300866-0.00406507300865628
301.71.70410609075807-0.00410609075806603
311.71.70878643000787-0.00878643000786932
321.711.71350778700708-0.0035077870070824
331.731.72490948325610.00509051674390125
341.731.72690948325610.00309051674390130
351.731.73158982250590-0.00158982250590198
361.741.734909483256100.0050905167439013
371.741.733475561773190.00652443822680838
381.741.74131226487132-0.00131226487132422
391.751.745271247121910.00472875287808552
401.781.758074639619950.0219253603800528
411.821.776919049867390.0430809501326104
421.831.793763460114830.0362365398851679
431.841.81205058436070.0279494156392993
441.851.826977030106960.0230229698930372
451.861.834977030106960.0250229698930372
461.861.847182118854010.0128178811459882
471.871.851862458103820.018137541896185
481.871.851780422605000.0182195773950046
491.871.860551589869140.00944841013086265
501.871.87178998921629-0.00178998921628635
511.871.87915066771589-0.0091506677158929
521.871.88174897146688-0.0117489714668766
531.871.89038829296727-0.0203882929672701
541.881.88682252572061-0.00682252572061463
551.881.88129777622337-0.00129777622336891
561.871.88261743697357-0.0126174369735655
571.871.88721574072455-0.0172157407245491
581.871.88581404447553-0.0158140444755328
591.871.89049438372534-0.020494383725336
601.871.89381404447553-0.0238140444755328
611.871.89238012299263-0.0223801229926257

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.58 & 1.58356282241847 & -0.00356282241847002 \tabularnewline
2 & 1.59 & 1.58799782926758 & 0.00200217073241852 \tabularnewline
3 & 1.6 & 1.59195681151817 & 0.00804318848182836 \tabularnewline
4 & 1.6 & 1.59795681151817 & 0.00204318848182837 \tabularnewline
5 & 1.6 & 1.60659613301857 & -0.00659613301856506 \tabularnewline
6 & 1.6 & 1.61663715076798 & -0.0166371507679749 \tabularnewline
7 & 1.61 & 1.61791579376876 & -0.00791579376876178 \tabularnewline
8 & 1.61 & 1.61583375826994 & -0.00583375826994225 \tabularnewline
9 & 1.62 & 1.62383375826994 & -0.00383375826994223 \tabularnewline
10 & 1.63 & 1.62243206202093 & 0.00756793797907393 \tabularnewline
11 & 1.63 & 1.62371070502171 & 0.006289294978287 \tabularnewline
12 & 1.63 & 1.63043206202093 & -0.000432062020926052 \tabularnewline
13 & 1.63 & 1.62899814053802 & 0.00100185946198101 \tabularnewline
14 & 1.63 & 1.63343314738714 & -0.00343314738713537 \tabularnewline
15 & 1.64 & 1.64079382588674 & -0.000793825886741844 \tabularnewline
16 & 1.64 & 1.64339212963773 & -0.0033921296377255 \tabularnewline
17 & 1.64 & 1.65203145113812 & -0.0120314511381190 \tabularnewline
18 & 1.65 & 1.65867077263851 & -0.00867077263851238 \tabularnewline
19 & 1.65 & 1.6599494156393 & -0.00994941563929933 \tabularnewline
20 & 1.65 & 1.65106398764245 & -0.00106398764244708 \tabularnewline
21 & 1.65 & 1.65906398764245 & -0.0090639876424471 \tabularnewline
22 & 1.65 & 1.65766229139343 & -0.00766229139343073 \tabularnewline
23 & 1.66 & 1.66234263064323 & -0.00234263064323399 \tabularnewline
24 & 1.67 & 1.66906398764245 & 0.000936012357552973 \tabularnewline
25 & 1.68 & 1.67103176240856 & 0.0089682375914437 \tabularnewline
26 & 1.68 & 1.67546676925767 & 0.00453323074232744 \tabularnewline
27 & 1.68 & 1.68282744775728 & -0.00282744775727914 \tabularnewline
28 & 1.68 & 1.68882744775728 & -0.00882744775727915 \tabularnewline
29 & 1.69 & 1.69406507300866 & -0.00406507300865628 \tabularnewline
30 & 1.7 & 1.70410609075807 & -0.00410609075806603 \tabularnewline
31 & 1.7 & 1.70878643000787 & -0.00878643000786932 \tabularnewline
32 & 1.71 & 1.71350778700708 & -0.0035077870070824 \tabularnewline
33 & 1.73 & 1.7249094832561 & 0.00509051674390125 \tabularnewline
34 & 1.73 & 1.7269094832561 & 0.00309051674390130 \tabularnewline
35 & 1.73 & 1.73158982250590 & -0.00158982250590198 \tabularnewline
36 & 1.74 & 1.73490948325610 & 0.0050905167439013 \tabularnewline
37 & 1.74 & 1.73347556177319 & 0.00652443822680838 \tabularnewline
38 & 1.74 & 1.74131226487132 & -0.00131226487132422 \tabularnewline
39 & 1.75 & 1.74527124712191 & 0.00472875287808552 \tabularnewline
40 & 1.78 & 1.75807463961995 & 0.0219253603800528 \tabularnewline
41 & 1.82 & 1.77691904986739 & 0.0430809501326104 \tabularnewline
42 & 1.83 & 1.79376346011483 & 0.0362365398851679 \tabularnewline
43 & 1.84 & 1.8120505843607 & 0.0279494156392993 \tabularnewline
44 & 1.85 & 1.82697703010696 & 0.0230229698930372 \tabularnewline
45 & 1.86 & 1.83497703010696 & 0.0250229698930372 \tabularnewline
46 & 1.86 & 1.84718211885401 & 0.0128178811459882 \tabularnewline
47 & 1.87 & 1.85186245810382 & 0.018137541896185 \tabularnewline
48 & 1.87 & 1.85178042260500 & 0.0182195773950046 \tabularnewline
49 & 1.87 & 1.86055158986914 & 0.00944841013086265 \tabularnewline
50 & 1.87 & 1.87178998921629 & -0.00178998921628635 \tabularnewline
51 & 1.87 & 1.87915066771589 & -0.0091506677158929 \tabularnewline
52 & 1.87 & 1.88174897146688 & -0.0117489714668766 \tabularnewline
53 & 1.87 & 1.89038829296727 & -0.0203882929672701 \tabularnewline
54 & 1.88 & 1.88682252572061 & -0.00682252572061463 \tabularnewline
55 & 1.88 & 1.88129777622337 & -0.00129777622336891 \tabularnewline
56 & 1.87 & 1.88261743697357 & -0.0126174369735655 \tabularnewline
57 & 1.87 & 1.88721574072455 & -0.0172157407245491 \tabularnewline
58 & 1.87 & 1.88581404447553 & -0.0158140444755328 \tabularnewline
59 & 1.87 & 1.89049438372534 & -0.020494383725336 \tabularnewline
60 & 1.87 & 1.89381404447553 & -0.0238140444755328 \tabularnewline
61 & 1.87 & 1.89238012299263 & -0.0223801229926257 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58047&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]1.58356282241847[/C][C]-0.00356282241847002[/C][/ROW]
[ROW][C]2[/C][C]1.59[/C][C]1.58799782926758[/C][C]0.00200217073241852[/C][/ROW]
[ROW][C]3[/C][C]1.6[/C][C]1.59195681151817[/C][C]0.00804318848182836[/C][/ROW]
[ROW][C]4[/C][C]1.6[/C][C]1.59795681151817[/C][C]0.00204318848182837[/C][/ROW]
[ROW][C]5[/C][C]1.6[/C][C]1.60659613301857[/C][C]-0.00659613301856506[/C][/ROW]
[ROW][C]6[/C][C]1.6[/C][C]1.61663715076798[/C][C]-0.0166371507679749[/C][/ROW]
[ROW][C]7[/C][C]1.61[/C][C]1.61791579376876[/C][C]-0.00791579376876178[/C][/ROW]
[ROW][C]8[/C][C]1.61[/C][C]1.61583375826994[/C][C]-0.00583375826994225[/C][/ROW]
[ROW][C]9[/C][C]1.62[/C][C]1.62383375826994[/C][C]-0.00383375826994223[/C][/ROW]
[ROW][C]10[/C][C]1.63[/C][C]1.62243206202093[/C][C]0.00756793797907393[/C][/ROW]
[ROW][C]11[/C][C]1.63[/C][C]1.62371070502171[/C][C]0.006289294978287[/C][/ROW]
[ROW][C]12[/C][C]1.63[/C][C]1.63043206202093[/C][C]-0.000432062020926052[/C][/ROW]
[ROW][C]13[/C][C]1.63[/C][C]1.62899814053802[/C][C]0.00100185946198101[/C][/ROW]
[ROW][C]14[/C][C]1.63[/C][C]1.63343314738714[/C][C]-0.00343314738713537[/C][/ROW]
[ROW][C]15[/C][C]1.64[/C][C]1.64079382588674[/C][C]-0.000793825886741844[/C][/ROW]
[ROW][C]16[/C][C]1.64[/C][C]1.64339212963773[/C][C]-0.0033921296377255[/C][/ROW]
[ROW][C]17[/C][C]1.64[/C][C]1.65203145113812[/C][C]-0.0120314511381190[/C][/ROW]
[ROW][C]18[/C][C]1.65[/C][C]1.65867077263851[/C][C]-0.00867077263851238[/C][/ROW]
[ROW][C]19[/C][C]1.65[/C][C]1.6599494156393[/C][C]-0.00994941563929933[/C][/ROW]
[ROW][C]20[/C][C]1.65[/C][C]1.65106398764245[/C][C]-0.00106398764244708[/C][/ROW]
[ROW][C]21[/C][C]1.65[/C][C]1.65906398764245[/C][C]-0.0090639876424471[/C][/ROW]
[ROW][C]22[/C][C]1.65[/C][C]1.65766229139343[/C][C]-0.00766229139343073[/C][/ROW]
[ROW][C]23[/C][C]1.66[/C][C]1.66234263064323[/C][C]-0.00234263064323399[/C][/ROW]
[ROW][C]24[/C][C]1.67[/C][C]1.66906398764245[/C][C]0.000936012357552973[/C][/ROW]
[ROW][C]25[/C][C]1.68[/C][C]1.67103176240856[/C][C]0.0089682375914437[/C][/ROW]
[ROW][C]26[/C][C]1.68[/C][C]1.67546676925767[/C][C]0.00453323074232744[/C][/ROW]
[ROW][C]27[/C][C]1.68[/C][C]1.68282744775728[/C][C]-0.00282744775727914[/C][/ROW]
[ROW][C]28[/C][C]1.68[/C][C]1.68882744775728[/C][C]-0.00882744775727915[/C][/ROW]
[ROW][C]29[/C][C]1.69[/C][C]1.69406507300866[/C][C]-0.00406507300865628[/C][/ROW]
[ROW][C]30[/C][C]1.7[/C][C]1.70410609075807[/C][C]-0.00410609075806603[/C][/ROW]
[ROW][C]31[/C][C]1.7[/C][C]1.70878643000787[/C][C]-0.00878643000786932[/C][/ROW]
[ROW][C]32[/C][C]1.71[/C][C]1.71350778700708[/C][C]-0.0035077870070824[/C][/ROW]
[ROW][C]33[/C][C]1.73[/C][C]1.7249094832561[/C][C]0.00509051674390125[/C][/ROW]
[ROW][C]34[/C][C]1.73[/C][C]1.7269094832561[/C][C]0.00309051674390130[/C][/ROW]
[ROW][C]35[/C][C]1.73[/C][C]1.73158982250590[/C][C]-0.00158982250590198[/C][/ROW]
[ROW][C]36[/C][C]1.74[/C][C]1.73490948325610[/C][C]0.0050905167439013[/C][/ROW]
[ROW][C]37[/C][C]1.74[/C][C]1.73347556177319[/C][C]0.00652443822680838[/C][/ROW]
[ROW][C]38[/C][C]1.74[/C][C]1.74131226487132[/C][C]-0.00131226487132422[/C][/ROW]
[ROW][C]39[/C][C]1.75[/C][C]1.74527124712191[/C][C]0.00472875287808552[/C][/ROW]
[ROW][C]40[/C][C]1.78[/C][C]1.75807463961995[/C][C]0.0219253603800528[/C][/ROW]
[ROW][C]41[/C][C]1.82[/C][C]1.77691904986739[/C][C]0.0430809501326104[/C][/ROW]
[ROW][C]42[/C][C]1.83[/C][C]1.79376346011483[/C][C]0.0362365398851679[/C][/ROW]
[ROW][C]43[/C][C]1.84[/C][C]1.8120505843607[/C][C]0.0279494156392993[/C][/ROW]
[ROW][C]44[/C][C]1.85[/C][C]1.82697703010696[/C][C]0.0230229698930372[/C][/ROW]
[ROW][C]45[/C][C]1.86[/C][C]1.83497703010696[/C][C]0.0250229698930372[/C][/ROW]
[ROW][C]46[/C][C]1.86[/C][C]1.84718211885401[/C][C]0.0128178811459882[/C][/ROW]
[ROW][C]47[/C][C]1.87[/C][C]1.85186245810382[/C][C]0.018137541896185[/C][/ROW]
[ROW][C]48[/C][C]1.87[/C][C]1.85178042260500[/C][C]0.0182195773950046[/C][/ROW]
[ROW][C]49[/C][C]1.87[/C][C]1.86055158986914[/C][C]0.00944841013086265[/C][/ROW]
[ROW][C]50[/C][C]1.87[/C][C]1.87178998921629[/C][C]-0.00178998921628635[/C][/ROW]
[ROW][C]51[/C][C]1.87[/C][C]1.87915066771589[/C][C]-0.0091506677158929[/C][/ROW]
[ROW][C]52[/C][C]1.87[/C][C]1.88174897146688[/C][C]-0.0117489714668766[/C][/ROW]
[ROW][C]53[/C][C]1.87[/C][C]1.89038829296727[/C][C]-0.0203882929672701[/C][/ROW]
[ROW][C]54[/C][C]1.88[/C][C]1.88682252572061[/C][C]-0.00682252572061463[/C][/ROW]
[ROW][C]55[/C][C]1.88[/C][C]1.88129777622337[/C][C]-0.00129777622336891[/C][/ROW]
[ROW][C]56[/C][C]1.87[/C][C]1.88261743697357[/C][C]-0.0126174369735655[/C][/ROW]
[ROW][C]57[/C][C]1.87[/C][C]1.88721574072455[/C][C]-0.0172157407245491[/C][/ROW]
[ROW][C]58[/C][C]1.87[/C][C]1.88581404447553[/C][C]-0.0158140444755328[/C][/ROW]
[ROW][C]59[/C][C]1.87[/C][C]1.89049438372534[/C][C]-0.020494383725336[/C][/ROW]
[ROW][C]60[/C][C]1.87[/C][C]1.89381404447553[/C][C]-0.0238140444755328[/C][/ROW]
[ROW][C]61[/C][C]1.87[/C][C]1.89238012299263[/C][C]-0.0223801229926257[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58047&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58047&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
11.581.58356282241847-0.00356282241847002
21.591.587997829267580.00200217073241852
31.61.591956811518170.00804318848182836
41.61.597956811518170.00204318848182837
51.61.60659613301857-0.00659613301856506
61.61.61663715076798-0.0166371507679749
71.611.61791579376876-0.00791579376876178
81.611.61583375826994-0.00583375826994225
91.621.62383375826994-0.00383375826994223
101.631.622432062020930.00756793797907393
111.631.623710705021710.006289294978287
121.631.63043206202093-0.000432062020926052
131.631.628998140538020.00100185946198101
141.631.63343314738714-0.00343314738713537
151.641.64079382588674-0.000793825886741844
161.641.64339212963773-0.0033921296377255
171.641.65203145113812-0.0120314511381190
181.651.65867077263851-0.00867077263851238
191.651.6599494156393-0.00994941563929933
201.651.65106398764245-0.00106398764244708
211.651.65906398764245-0.0090639876424471
221.651.65766229139343-0.00766229139343073
231.661.66234263064323-0.00234263064323399
241.671.669063987642450.000936012357552973
251.681.671031762408560.0089682375914437
261.681.675466769257670.00453323074232744
271.681.68282744775728-0.00282744775727914
281.681.68882744775728-0.00882744775727915
291.691.69406507300866-0.00406507300865628
301.71.70410609075807-0.00410609075806603
311.71.70878643000787-0.00878643000786932
321.711.71350778700708-0.0035077870070824
331.731.72490948325610.00509051674390125
341.731.72690948325610.00309051674390130
351.731.73158982250590-0.00158982250590198
361.741.734909483256100.0050905167439013
371.741.733475561773190.00652443822680838
381.741.74131226487132-0.00131226487132422
391.751.745271247121910.00472875287808552
401.781.758074639619950.0219253603800528
411.821.776919049867390.0430809501326104
421.831.793763460114830.0362365398851679
431.841.81205058436070.0279494156392993
441.851.826977030106960.0230229698930372
451.861.834977030106960.0250229698930372
461.861.847182118854010.0128178811459882
471.871.851862458103820.018137541896185
481.871.851780422605000.0182195773950046
491.871.860551589869140.00944841013086265
501.871.87178998921629-0.00178998921628635
511.871.87915066771589-0.0091506677158929
521.871.88174897146688-0.0117489714668766
531.871.89038829296727-0.0203882929672701
541.881.88682252572061-0.00682252572061463
551.881.88129777622337-0.00129777622336891
561.871.88261743697357-0.0126174369735655
571.871.88721574072455-0.0172157407245491
581.871.88581404447553-0.0158140444755328
591.871.89049438372534-0.020494383725336
601.871.89381404447553-0.0238140444755328
611.871.89238012299263-0.0223801229926257







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.01075724515567270.02151449031134540.989242754844327
180.002328762142625280.004657524285250560.997671237857375
190.001115959582480400.002231919164960810.99888404041752
200.0003447160138245510.0006894320276491020.999655283986175
210.0003519454954302770.0007038909908605540.99964805450457
220.0008571141923655670.001714228384731130.999142885807634
230.0003199415490880360.0006398830981760720.999680058450912
240.0001180627926636750.0002361255853273490.999881937207336
250.0002004077138991020.0004008154277982040.9997995922861
268.08745333451187e-050.0001617490666902370.999919125466655
273.47444651721434e-056.94889303442868e-050.999965255534828
282.18048072190983e-054.36096144381967e-050.999978195192781
291.54315079395946e-053.08630158791891e-050.99998456849206
302.27202331958586e-054.54404663917173e-050.999977279766804
313.12256941957e-056.24513883914e-050.999968774305804
322.67514855442952e-055.35029710885903e-050.999973248514456
333.22906662037787e-056.45813324075575e-050.999967709333796
342.42471905199030e-054.84943810398061e-050.99997575280948
358.57138946197005e-050.0001714277892394010.99991428610538
360.0002326963897577520.0004653927795155050.999767303610242
370.001401921661017920.002803843322035840.998598078338982
380.01314154535670580.02628309071341150.986858454643294
390.1303731771808160.2607463543616320.869626822819184
400.5755730624005010.8488538751989980.424426937599499
410.9387232769450660.1225534461098680.061276723054934
420.919095889737590.1618082205248200.0809041102624099
430.9245293362229130.1509413275541740.0754706637770872
440.91620730457240.1675853908552000.0837926954276002

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.0107572451556727 & 0.0215144903113454 & 0.989242754844327 \tabularnewline
18 & 0.00232876214262528 & 0.00465752428525056 & 0.997671237857375 \tabularnewline
19 & 0.00111595958248040 & 0.00223191916496081 & 0.99888404041752 \tabularnewline
20 & 0.000344716013824551 & 0.000689432027649102 & 0.999655283986175 \tabularnewline
21 & 0.000351945495430277 & 0.000703890990860554 & 0.99964805450457 \tabularnewline
22 & 0.000857114192365567 & 0.00171422838473113 & 0.999142885807634 \tabularnewline
23 & 0.000319941549088036 & 0.000639883098176072 & 0.999680058450912 \tabularnewline
24 & 0.000118062792663675 & 0.000236125585327349 & 0.999881937207336 \tabularnewline
25 & 0.000200407713899102 & 0.000400815427798204 & 0.9997995922861 \tabularnewline
26 & 8.08745333451187e-05 & 0.000161749066690237 & 0.999919125466655 \tabularnewline
27 & 3.47444651721434e-05 & 6.94889303442868e-05 & 0.999965255534828 \tabularnewline
28 & 2.18048072190983e-05 & 4.36096144381967e-05 & 0.999978195192781 \tabularnewline
29 & 1.54315079395946e-05 & 3.08630158791891e-05 & 0.99998456849206 \tabularnewline
30 & 2.27202331958586e-05 & 4.54404663917173e-05 & 0.999977279766804 \tabularnewline
31 & 3.12256941957e-05 & 6.24513883914e-05 & 0.999968774305804 \tabularnewline
32 & 2.67514855442952e-05 & 5.35029710885903e-05 & 0.999973248514456 \tabularnewline
33 & 3.22906662037787e-05 & 6.45813324075575e-05 & 0.999967709333796 \tabularnewline
34 & 2.42471905199030e-05 & 4.84943810398061e-05 & 0.99997575280948 \tabularnewline
35 & 8.57138946197005e-05 & 0.000171427789239401 & 0.99991428610538 \tabularnewline
36 & 0.000232696389757752 & 0.000465392779515505 & 0.999767303610242 \tabularnewline
37 & 0.00140192166101792 & 0.00280384332203584 & 0.998598078338982 \tabularnewline
38 & 0.0131415453567058 & 0.0262830907134115 & 0.986858454643294 \tabularnewline
39 & 0.130373177180816 & 0.260746354361632 & 0.869626822819184 \tabularnewline
40 & 0.575573062400501 & 0.848853875198998 & 0.424426937599499 \tabularnewline
41 & 0.938723276945066 & 0.122553446109868 & 0.061276723054934 \tabularnewline
42 & 0.91909588973759 & 0.161808220524820 & 0.0809041102624099 \tabularnewline
43 & 0.924529336222913 & 0.150941327554174 & 0.0754706637770872 \tabularnewline
44 & 0.9162073045724 & 0.167585390855200 & 0.0837926954276002 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58047&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]17[/C][C]0.0107572451556727[/C][C]0.0215144903113454[/C][C]0.989242754844327[/C][/ROW]
[ROW][C]18[/C][C]0.00232876214262528[/C][C]0.00465752428525056[/C][C]0.997671237857375[/C][/ROW]
[ROW][C]19[/C][C]0.00111595958248040[/C][C]0.00223191916496081[/C][C]0.99888404041752[/C][/ROW]
[ROW][C]20[/C][C]0.000344716013824551[/C][C]0.000689432027649102[/C][C]0.999655283986175[/C][/ROW]
[ROW][C]21[/C][C]0.000351945495430277[/C][C]0.000703890990860554[/C][C]0.99964805450457[/C][/ROW]
[ROW][C]22[/C][C]0.000857114192365567[/C][C]0.00171422838473113[/C][C]0.999142885807634[/C][/ROW]
[ROW][C]23[/C][C]0.000319941549088036[/C][C]0.000639883098176072[/C][C]0.999680058450912[/C][/ROW]
[ROW][C]24[/C][C]0.000118062792663675[/C][C]0.000236125585327349[/C][C]0.999881937207336[/C][/ROW]
[ROW][C]25[/C][C]0.000200407713899102[/C][C]0.000400815427798204[/C][C]0.9997995922861[/C][/ROW]
[ROW][C]26[/C][C]8.08745333451187e-05[/C][C]0.000161749066690237[/C][C]0.999919125466655[/C][/ROW]
[ROW][C]27[/C][C]3.47444651721434e-05[/C][C]6.94889303442868e-05[/C][C]0.999965255534828[/C][/ROW]
[ROW][C]28[/C][C]2.18048072190983e-05[/C][C]4.36096144381967e-05[/C][C]0.999978195192781[/C][/ROW]
[ROW][C]29[/C][C]1.54315079395946e-05[/C][C]3.08630158791891e-05[/C][C]0.99998456849206[/C][/ROW]
[ROW][C]30[/C][C]2.27202331958586e-05[/C][C]4.54404663917173e-05[/C][C]0.999977279766804[/C][/ROW]
[ROW][C]31[/C][C]3.12256941957e-05[/C][C]6.24513883914e-05[/C][C]0.999968774305804[/C][/ROW]
[ROW][C]32[/C][C]2.67514855442952e-05[/C][C]5.35029710885903e-05[/C][C]0.999973248514456[/C][/ROW]
[ROW][C]33[/C][C]3.22906662037787e-05[/C][C]6.45813324075575e-05[/C][C]0.999967709333796[/C][/ROW]
[ROW][C]34[/C][C]2.42471905199030e-05[/C][C]4.84943810398061e-05[/C][C]0.99997575280948[/C][/ROW]
[ROW][C]35[/C][C]8.57138946197005e-05[/C][C]0.000171427789239401[/C][C]0.99991428610538[/C][/ROW]
[ROW][C]36[/C][C]0.000232696389757752[/C][C]0.000465392779515505[/C][C]0.999767303610242[/C][/ROW]
[ROW][C]37[/C][C]0.00140192166101792[/C][C]0.00280384332203584[/C][C]0.998598078338982[/C][/ROW]
[ROW][C]38[/C][C]0.0131415453567058[/C][C]0.0262830907134115[/C][C]0.986858454643294[/C][/ROW]
[ROW][C]39[/C][C]0.130373177180816[/C][C]0.260746354361632[/C][C]0.869626822819184[/C][/ROW]
[ROW][C]40[/C][C]0.575573062400501[/C][C]0.848853875198998[/C][C]0.424426937599499[/C][/ROW]
[ROW][C]41[/C][C]0.938723276945066[/C][C]0.122553446109868[/C][C]0.061276723054934[/C][/ROW]
[ROW][C]42[/C][C]0.91909588973759[/C][C]0.161808220524820[/C][C]0.0809041102624099[/C][/ROW]
[ROW][C]43[/C][C]0.924529336222913[/C][C]0.150941327554174[/C][C]0.0754706637770872[/C][/ROW]
[ROW][C]44[/C][C]0.9162073045724[/C][C]0.167585390855200[/C][C]0.0837926954276002[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58047&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58047&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
170.01075724515567270.02151449031134540.989242754844327
180.002328762142625280.004657524285250560.997671237857375
190.001115959582480400.002231919164960810.99888404041752
200.0003447160138245510.0006894320276491020.999655283986175
210.0003519454954302770.0007038909908605540.99964805450457
220.0008571141923655670.001714228384731130.999142885807634
230.0003199415490880360.0006398830981760720.999680058450912
240.0001180627926636750.0002361255853273490.999881937207336
250.0002004077138991020.0004008154277982040.9997995922861
268.08745333451187e-050.0001617490666902370.999919125466655
273.47444651721434e-056.94889303442868e-050.999965255534828
282.18048072190983e-054.36096144381967e-050.999978195192781
291.54315079395946e-053.08630158791891e-050.99998456849206
302.27202331958586e-054.54404663917173e-050.999977279766804
313.12256941957e-056.24513883914e-050.999968774305804
322.67514855442952e-055.35029710885903e-050.999973248514456
333.22906662037787e-056.45813324075575e-050.999967709333796
342.42471905199030e-054.84943810398061e-050.99997575280948
358.57138946197005e-050.0001714277892394010.99991428610538
360.0002326963897577520.0004653927795155050.999767303610242
370.001401921661017920.002803843322035840.998598078338982
380.01314154535670580.02628309071341150.986858454643294
390.1303731771808160.2607463543616320.869626822819184
400.5755730624005010.8488538751989980.424426937599499
410.9387232769450660.1225534461098680.061276723054934
420.919095889737590.1618082205248200.0809041102624099
430.9245293362229130.1509413275541740.0754706637770872
440.91620730457240.1675853908552000.0837926954276002







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level200.714285714285714NOK
5% type I error level220.785714285714286NOK
10% type I error level220.785714285714286NOK

\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 & 20 & 0.714285714285714 & NOK \tabularnewline
5% type I error level & 22 & 0.785714285714286 & NOK \tabularnewline
10% type I error level & 22 & 0.785714285714286 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58047&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]20[/C][C]0.714285714285714[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]22[/C][C]0.785714285714286[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]22[/C][C]0.785714285714286[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58047&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58047&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 level200.714285714285714NOK
5% type I error level220.785714285714286NOK
10% type I error level220.785714285714286NOK



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