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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 computationTue, 29 Nov 2011 19:52:26 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/29/t1322614372903c4tii67ujls3.htm/, Retrieved Fri, 29 Mar 2024 02:09:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=148791, Retrieved Fri, 29 Mar 2024 02:09:17 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact65
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Model 1 Monthly d...] [2011-11-30 00:32:47] [b9f5bf8f9089a40337275cf2fd2f13a1]
- R  D    [Multiple Regression] [Model 2 WS8] [2011-11-30 00:52:26] [766d4bb4f790a2464f86fcafbf87a680] [Current]
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Dataseries X:
9743    9084    9081    9700
8587    9743    9084    9081
9731    8587    9743    9084
9563    9731    8587    9743
9998    9563    9731    8587
9437    9998    9563    9731
10038    9437    9998    9563
9918    10038    9437    9998
9252    9918    10038    9437
9737    9252    9918    10038
9035    9737    9252    9918
9133    9035    9737    9252
9487    9133    9035    9737
8700    9487    9133    9035
9627    8700    9487    9133
8947    9627    8700    9487
9283    8947    9627    8700
8829    9283    8947    9627
9947    8829    9283    8947
9628    9947    8829    9283
9318    9628    9947    8829
9605    9318    9628    9947
8640    9605    9318    9628
9214    8640    9605    9318
9567    9214    8640    9605
8547    9567    9214    8640
9185    8547    9567    9214
9470    9185    8547    9567
9123    9470    9185    8547
9278    9123    9470    9185
10170    9278    9123    9470
9434    10170    9278    9123
9655    9434    10170    9278
9429    9655    9434    10170
8739    9429    9655    9434
9552    8739    9429    9655
9687    9552    8739    9429
9019    9687    9552    8739
9672    9019    9687    9552
9206    9672    9019    9687
9069    9206    9672    9019
9788    9069    9206    9672
10312    9788    9069    9206
10105    10312    9788    9069
9863    10105    10312    9788
9656    9863    10105    10312
9295    9656    9863    10105
9946    9295    9656    9863
9701    9946    9295    9656
9049    9701    9946    9295
10190    9049    9701    9946
9706    10190    9049    9701
9765    9706    10190    9049
9893    9765    9706    10190
9994    9893    9765    9706
10433    9994    9893    9765
10073    10433    9994    9893
10112    10073    10433    9994
9266    10112    10073    10433
9820    9266    10112    10073
10097    9820    9266    10112
9115    10097    9820    9266
10411    9115    10097    9820
9678    10411    9115    10097
10408    9678    10411    9115
10153    10408    9678    10411
10368    10153    10408    9678
10581    10368    10153    10408
10597    10581    10368    10153
10680    10597    10581    10368
9738    10680    10597    10581
9556    9738    10680    10597




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'George Udny Yule' @ yule.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148791&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148791&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148791&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'George Udny Yule' @ yule.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Yt[t] = + 3540.69197220564 + 0.130932028247943`Yt-1`[t] + 0.201818490077341`Yt-2`[t] + 0.264823448542266`Yt-3`[t] + 385.773255960303M1[t] -436.300069757011M2[t] + 469.50316191611M3[t] + 72.8939494758225M4[t] + 333.401103073757M5[t] + 79.6860766985085M6[t] + 718.477094565067M7[t] + 477.862169357032M8[t] + 160.878337540301M9[t] + 134.048534247598M10[t] -554.848198896825M11[t] + 5.16340774563458t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Yt[t] =  +  3540.69197220564 +  0.130932028247943`Yt-1`[t] +  0.201818490077341`Yt-2`[t] +  0.264823448542266`Yt-3`[t] +  385.773255960303M1[t] -436.300069757011M2[t] +  469.50316191611M3[t] +  72.8939494758225M4[t] +  333.401103073757M5[t] +  79.6860766985085M6[t] +  718.477094565067M7[t] +  477.862169357032M8[t] +  160.878337540301M9[t] +  134.048534247598M10[t] -554.848198896825M11[t] +  5.16340774563458t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148791&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Yt[t] =  +  3540.69197220564 +  0.130932028247943`Yt-1`[t] +  0.201818490077341`Yt-2`[t] +  0.264823448542266`Yt-3`[t] +  385.773255960303M1[t] -436.300069757011M2[t] +  469.50316191611M3[t] +  72.8939494758225M4[t] +  333.401103073757M5[t] +  79.6860766985085M6[t] +  718.477094565067M7[t] +  477.862169357032M8[t] +  160.878337540301M9[t] +  134.048534247598M10[t] -554.848198896825M11[t] +  5.16340774563458t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148791&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148791&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
Yt[t] = + 3540.69197220564 + 0.130932028247943`Yt-1`[t] + 0.201818490077341`Yt-2`[t] + 0.264823448542266`Yt-3`[t] + 385.773255960303M1[t] -436.300069757011M2[t] + 469.50316191611M3[t] + 72.8939494758225M4[t] + 333.401103073757M5[t] + 79.6860766985085M6[t] + 718.477094565067M7[t] + 477.862169357032M8[t] + 160.878337540301M9[t] + 134.048534247598M10[t] -554.848198896825M11[t] + 5.16340774563458t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3540.691972205641487.8911512.37970.0207590.01038
`Yt-1`0.1309320282479430.1339030.97780.3323710.166186
`Yt-2`0.2018184900773410.1290491.56390.1234760.061738
`Yt-3`0.2648234485422660.1342511.97260.0534860.026743
M1385.773255960303200.1878081.92710.0590520.029526
M2-436.300069757011220.764862-1.97630.0530530.026527
M3469.50316191611159.0440282.9520.0046050.002303
M472.8939494758225235.720340.30920.7582870.379144
M5333.401103073757212.066041.57220.1215480.060774
M679.6860766985085183.3585010.43460.6655290.332764
M7718.477094565067182.2649613.94190.0002270.000113
M8477.862169357032222.7425832.14540.0362750.018138
M9160.878337540301205.8738380.78140.4378340.218917
M10134.048534247598177.7129110.75430.453830.226915
M11-554.848198896825184.793507-3.00250.0039960.001998
t5.163407745634582.4226492.13130.0374670.018733

\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) & 3540.69197220564 & 1487.891151 & 2.3797 & 0.020759 & 0.01038 \tabularnewline
`Yt-1` & 0.130932028247943 & 0.133903 & 0.9778 & 0.332371 & 0.166186 \tabularnewline
`Yt-2` & 0.201818490077341 & 0.129049 & 1.5639 & 0.123476 & 0.061738 \tabularnewline
`Yt-3` & 0.264823448542266 & 0.134251 & 1.9726 & 0.053486 & 0.026743 \tabularnewline
M1 & 385.773255960303 & 200.187808 & 1.9271 & 0.059052 & 0.029526 \tabularnewline
M2 & -436.300069757011 & 220.764862 & -1.9763 & 0.053053 & 0.026527 \tabularnewline
M3 & 469.50316191611 & 159.044028 & 2.952 & 0.004605 & 0.002303 \tabularnewline
M4 & 72.8939494758225 & 235.72034 & 0.3092 & 0.758287 & 0.379144 \tabularnewline
M5 & 333.401103073757 & 212.06604 & 1.5722 & 0.121548 & 0.060774 \tabularnewline
M6 & 79.6860766985085 & 183.358501 & 0.4346 & 0.665529 & 0.332764 \tabularnewline
M7 & 718.477094565067 & 182.264961 & 3.9419 & 0.000227 & 0.000113 \tabularnewline
M8 & 477.862169357032 & 222.742583 & 2.1454 & 0.036275 & 0.018138 \tabularnewline
M9 & 160.878337540301 & 205.873838 & 0.7814 & 0.437834 & 0.218917 \tabularnewline
M10 & 134.048534247598 & 177.712911 & 0.7543 & 0.45383 & 0.226915 \tabularnewline
M11 & -554.848198896825 & 184.793507 & -3.0025 & 0.003996 & 0.001998 \tabularnewline
t & 5.16340774563458 & 2.422649 & 2.1313 & 0.037467 & 0.018733 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148791&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]3540.69197220564[/C][C]1487.891151[/C][C]2.3797[/C][C]0.020759[/C][C]0.01038[/C][/ROW]
[ROW][C]`Yt-1`[/C][C]0.130932028247943[/C][C]0.133903[/C][C]0.9778[/C][C]0.332371[/C][C]0.166186[/C][/ROW]
[ROW][C]`Yt-2`[/C][C]0.201818490077341[/C][C]0.129049[/C][C]1.5639[/C][C]0.123476[/C][C]0.061738[/C][/ROW]
[ROW][C]`Yt-3`[/C][C]0.264823448542266[/C][C]0.134251[/C][C]1.9726[/C][C]0.053486[/C][C]0.026743[/C][/ROW]
[ROW][C]M1[/C][C]385.773255960303[/C][C]200.187808[/C][C]1.9271[/C][C]0.059052[/C][C]0.029526[/C][/ROW]
[ROW][C]M2[/C][C]-436.300069757011[/C][C]220.764862[/C][C]-1.9763[/C][C]0.053053[/C][C]0.026527[/C][/ROW]
[ROW][C]M3[/C][C]469.50316191611[/C][C]159.044028[/C][C]2.952[/C][C]0.004605[/C][C]0.002303[/C][/ROW]
[ROW][C]M4[/C][C]72.8939494758225[/C][C]235.72034[/C][C]0.3092[/C][C]0.758287[/C][C]0.379144[/C][/ROW]
[ROW][C]M5[/C][C]333.401103073757[/C][C]212.06604[/C][C]1.5722[/C][C]0.121548[/C][C]0.060774[/C][/ROW]
[ROW][C]M6[/C][C]79.6860766985085[/C][C]183.358501[/C][C]0.4346[/C][C]0.665529[/C][C]0.332764[/C][/ROW]
[ROW][C]M7[/C][C]718.477094565067[/C][C]182.264961[/C][C]3.9419[/C][C]0.000227[/C][C]0.000113[/C][/ROW]
[ROW][C]M8[/C][C]477.862169357032[/C][C]222.742583[/C][C]2.1454[/C][C]0.036275[/C][C]0.018138[/C][/ROW]
[ROW][C]M9[/C][C]160.878337540301[/C][C]205.873838[/C][C]0.7814[/C][C]0.437834[/C][C]0.218917[/C][/ROW]
[ROW][C]M10[/C][C]134.048534247598[/C][C]177.712911[/C][C]0.7543[/C][C]0.45383[/C][C]0.226915[/C][/ROW]
[ROW][C]M11[/C][C]-554.848198896825[/C][C]184.793507[/C][C]-3.0025[/C][C]0.003996[/C][C]0.001998[/C][/ROW]
[ROW][C]t[/C][C]5.16340774563458[/C][C]2.422649[/C][C]2.1313[/C][C]0.037467[/C][C]0.018733[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148791&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148791&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)3540.691972205641487.8911512.37970.0207590.01038
`Yt-1`0.1309320282479430.1339030.97780.3323710.166186
`Yt-2`0.2018184900773410.1290491.56390.1234760.061738
`Yt-3`0.2648234485422660.1342511.97260.0534860.026743
M1385.773255960303200.1878081.92710.0590520.029526
M2-436.300069757011220.764862-1.97630.0530530.026527
M3469.50316191611159.0440282.9520.0046050.002303
M472.8939494758225235.720340.30920.7582870.379144
M5333.401103073757212.066041.57220.1215480.060774
M679.6860766985085183.3585010.43460.6655290.332764
M7718.477094565067182.2649613.94190.0002270.000113
M8477.862169357032222.7425832.14540.0362750.018138
M9160.878337540301205.8738380.78140.4378340.218917
M10134.048534247598177.7129110.75430.453830.226915
M11-554.848198896825184.793507-3.00250.0039960.001998
t5.163407745634582.4226492.13130.0374670.018733







Multiple Linear Regression - Regression Statistics
Multiple R0.881471388071222
R-squared0.776991807988208
Adjusted R-squared0.717257470842192
F-TEST (value)13.0074567679376
F-TEST (DF numerator)15
F-TEST (DF denominator)56
p-value3.57713858534225e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation268.914224668372
Sum Squared Residuals4049632.17282355

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.881471388071222 \tabularnewline
R-squared & 0.776991807988208 \tabularnewline
Adjusted R-squared & 0.717257470842192 \tabularnewline
F-TEST (value) & 13.0074567679376 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 56 \tabularnewline
p-value & 3.57713858534225e-13 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 268.914224668372 \tabularnewline
Sum Squared Residuals & 4049632.17282355 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148791&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.881471388071222[/C][/ROW]
[ROW][C]R-squared[/C][C]0.776991807988208[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.717257470842192[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]13.0074567679376[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]56[/C][/ROW]
[ROW][C]p-value[/C][C]3.57713858534225e-13[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]268.914224668372[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]4049632.17282355[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148791&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148791&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 R0.881471388071222
R-squared0.776991807988208
Adjusted R-squared0.717257470842192
F-TEST (value)13.0074567679376
F-TEST (DF numerator)15
F-TEST (DF denominator)56
p-value3.57713858534225e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation268.914224668372
Sum Squared Residuals4049632.17282355







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
197439522.51633976821220.48366023179
285878628.5703692345-41.5703692344929
397319521.97243930522209.02756069478
495639221.52935298616341.470647013838
599989389.9477797177608.052220282305
694379467.4041121753-30.4041121752958
71003810081.2063737689-43.2063737689353
899189926.42303246605-8.42303246604665
992529571.61772290947-319.617722909468
1097379597.69127031389139.30872968611
1190358811.27005039877223.729949601227
1291339200.87692416954-67.8769241695368
1394879591.40771915248-104.407719152479
1487008654.7198903314845.28010966852
1596279560.0394669636266.9605330363752
1689479224.88400154791-277.88400154791
1792839380.19046998181-97.1904699818093
1888299283.88677638959-454.886776389594
1999479756.12912883447190.870871165534
2096289664.41470316836-36.414703168355
2193189416.23018835444-98.230188354443
2296059585.667381186119.3326188139058
2386408792.4691358855-152.469135885506
2492149201.957972872812.0420271272051
2595679549.2991076000517.7008923999518
2685478638.897381061-91.8973810609998
2791859639.56393812742-454.563938127416
2894709219.28058491148250.719415088517
2991239380.90705346195-257.907053461947
3092789313.3976508723-35.3976508723052
31101709983.09020764064186.909792359361
3294349803.81818869323-369.818188693225
3396559616.7015195046838.2984804953184
3494299711.65520960319-282.655209603187
3587398848.02307400035-109.023074000348
3695529330.60658452209221.393415477912
3796879628.8861296696958.1138703303142
3890198811.0022884502207.997711549807
3996729877.05329282463-205.053292824627
4092069472.04251675742-266.042516757423
4190699631.58416333172-562.58416333172
4297889443.9771523542344.022847645803
431031210031.0148461154280.98515388463
44101059972.99839337021132.001606629791
4598639930.2359877542-67.2359877542048
4696569973.87510096127-317.875100961272
4792959159.38031726819135.619682731807
4899469566.2617597199379.738240280092
4997019914.76024504909-213.760245049086
5090499101.55455227325-52.554552273252
511019010050.1080442064139.891955793584
5297069611.5882833193894.4117166806154
5397659871.49775171964-106.497751719637
5498939835.1545283459557.8454716540549
55999410379.600995394-385.600995393981
561043310198.8309629785234.169037021484
57100739998.7707682194974.2292317805112
581011210045.314327949966.6856720501176
5992669410.29018913498-144.290189134976
6098209772.0677795174847.9322204825245
611009710075.130458760521.8695412395087
6291159182.25551864958-67.2555186495819
631041110167.2628185727243.737181427304
6496789820.67526047764-142.675260477637
65104089991.87278178719416.127218212808
661015310034.1797798627118.820220137337
671036810597.9584482466-229.958448246608
681058110532.514719323648.4852806763519
691059710224.4438132577372.556186742286
701068010304.7967099857375.203290014327
7197389691.567233312246.4327666877956
72955610149.2289791982-593.228979198197

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 9743 & 9522.51633976821 & 220.48366023179 \tabularnewline
2 & 8587 & 8628.5703692345 & -41.5703692344929 \tabularnewline
3 & 9731 & 9521.97243930522 & 209.02756069478 \tabularnewline
4 & 9563 & 9221.52935298616 & 341.470647013838 \tabularnewline
5 & 9998 & 9389.9477797177 & 608.052220282305 \tabularnewline
6 & 9437 & 9467.4041121753 & -30.4041121752958 \tabularnewline
7 & 10038 & 10081.2063737689 & -43.2063737689353 \tabularnewline
8 & 9918 & 9926.42303246605 & -8.42303246604665 \tabularnewline
9 & 9252 & 9571.61772290947 & -319.617722909468 \tabularnewline
10 & 9737 & 9597.69127031389 & 139.30872968611 \tabularnewline
11 & 9035 & 8811.27005039877 & 223.729949601227 \tabularnewline
12 & 9133 & 9200.87692416954 & -67.8769241695368 \tabularnewline
13 & 9487 & 9591.40771915248 & -104.407719152479 \tabularnewline
14 & 8700 & 8654.71989033148 & 45.28010966852 \tabularnewline
15 & 9627 & 9560.03946696362 & 66.9605330363752 \tabularnewline
16 & 8947 & 9224.88400154791 & -277.88400154791 \tabularnewline
17 & 9283 & 9380.19046998181 & -97.1904699818093 \tabularnewline
18 & 8829 & 9283.88677638959 & -454.886776389594 \tabularnewline
19 & 9947 & 9756.12912883447 & 190.870871165534 \tabularnewline
20 & 9628 & 9664.41470316836 & -36.414703168355 \tabularnewline
21 & 9318 & 9416.23018835444 & -98.230188354443 \tabularnewline
22 & 9605 & 9585.6673811861 & 19.3326188139058 \tabularnewline
23 & 8640 & 8792.4691358855 & -152.469135885506 \tabularnewline
24 & 9214 & 9201.9579728728 & 12.0420271272051 \tabularnewline
25 & 9567 & 9549.29910760005 & 17.7008923999518 \tabularnewline
26 & 8547 & 8638.897381061 & -91.8973810609998 \tabularnewline
27 & 9185 & 9639.56393812742 & -454.563938127416 \tabularnewline
28 & 9470 & 9219.28058491148 & 250.719415088517 \tabularnewline
29 & 9123 & 9380.90705346195 & -257.907053461947 \tabularnewline
30 & 9278 & 9313.3976508723 & -35.3976508723052 \tabularnewline
31 & 10170 & 9983.09020764064 & 186.909792359361 \tabularnewline
32 & 9434 & 9803.81818869323 & -369.818188693225 \tabularnewline
33 & 9655 & 9616.70151950468 & 38.2984804953184 \tabularnewline
34 & 9429 & 9711.65520960319 & -282.655209603187 \tabularnewline
35 & 8739 & 8848.02307400035 & -109.023074000348 \tabularnewline
36 & 9552 & 9330.60658452209 & 221.393415477912 \tabularnewline
37 & 9687 & 9628.88612966969 & 58.1138703303142 \tabularnewline
38 & 9019 & 8811.0022884502 & 207.997711549807 \tabularnewline
39 & 9672 & 9877.05329282463 & -205.053292824627 \tabularnewline
40 & 9206 & 9472.04251675742 & -266.042516757423 \tabularnewline
41 & 9069 & 9631.58416333172 & -562.58416333172 \tabularnewline
42 & 9788 & 9443.9771523542 & 344.022847645803 \tabularnewline
43 & 10312 & 10031.0148461154 & 280.98515388463 \tabularnewline
44 & 10105 & 9972.99839337021 & 132.001606629791 \tabularnewline
45 & 9863 & 9930.2359877542 & -67.2359877542048 \tabularnewline
46 & 9656 & 9973.87510096127 & -317.875100961272 \tabularnewline
47 & 9295 & 9159.38031726819 & 135.619682731807 \tabularnewline
48 & 9946 & 9566.2617597199 & 379.738240280092 \tabularnewline
49 & 9701 & 9914.76024504909 & -213.760245049086 \tabularnewline
50 & 9049 & 9101.55455227325 & -52.554552273252 \tabularnewline
51 & 10190 & 10050.1080442064 & 139.891955793584 \tabularnewline
52 & 9706 & 9611.58828331938 & 94.4117166806154 \tabularnewline
53 & 9765 & 9871.49775171964 & -106.497751719637 \tabularnewline
54 & 9893 & 9835.15452834595 & 57.8454716540549 \tabularnewline
55 & 9994 & 10379.600995394 & -385.600995393981 \tabularnewline
56 & 10433 & 10198.8309629785 & 234.169037021484 \tabularnewline
57 & 10073 & 9998.77076821949 & 74.2292317805112 \tabularnewline
58 & 10112 & 10045.3143279499 & 66.6856720501176 \tabularnewline
59 & 9266 & 9410.29018913498 & -144.290189134976 \tabularnewline
60 & 9820 & 9772.06777951748 & 47.9322204825245 \tabularnewline
61 & 10097 & 10075.1304587605 & 21.8695412395087 \tabularnewline
62 & 9115 & 9182.25551864958 & -67.2555186495819 \tabularnewline
63 & 10411 & 10167.2628185727 & 243.737181427304 \tabularnewline
64 & 9678 & 9820.67526047764 & -142.675260477637 \tabularnewline
65 & 10408 & 9991.87278178719 & 416.127218212808 \tabularnewline
66 & 10153 & 10034.1797798627 & 118.820220137337 \tabularnewline
67 & 10368 & 10597.9584482466 & -229.958448246608 \tabularnewline
68 & 10581 & 10532.5147193236 & 48.4852806763519 \tabularnewline
69 & 10597 & 10224.4438132577 & 372.556186742286 \tabularnewline
70 & 10680 & 10304.7967099857 & 375.203290014327 \tabularnewline
71 & 9738 & 9691.5672333122 & 46.4327666877956 \tabularnewline
72 & 9556 & 10149.2289791982 & -593.228979198197 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148791&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]9743[/C][C]9522.51633976821[/C][C]220.48366023179[/C][/ROW]
[ROW][C]2[/C][C]8587[/C][C]8628.5703692345[/C][C]-41.5703692344929[/C][/ROW]
[ROW][C]3[/C][C]9731[/C][C]9521.97243930522[/C][C]209.02756069478[/C][/ROW]
[ROW][C]4[/C][C]9563[/C][C]9221.52935298616[/C][C]341.470647013838[/C][/ROW]
[ROW][C]5[/C][C]9998[/C][C]9389.9477797177[/C][C]608.052220282305[/C][/ROW]
[ROW][C]6[/C][C]9437[/C][C]9467.4041121753[/C][C]-30.4041121752958[/C][/ROW]
[ROW][C]7[/C][C]10038[/C][C]10081.2063737689[/C][C]-43.2063737689353[/C][/ROW]
[ROW][C]8[/C][C]9918[/C][C]9926.42303246605[/C][C]-8.42303246604665[/C][/ROW]
[ROW][C]9[/C][C]9252[/C][C]9571.61772290947[/C][C]-319.617722909468[/C][/ROW]
[ROW][C]10[/C][C]9737[/C][C]9597.69127031389[/C][C]139.30872968611[/C][/ROW]
[ROW][C]11[/C][C]9035[/C][C]8811.27005039877[/C][C]223.729949601227[/C][/ROW]
[ROW][C]12[/C][C]9133[/C][C]9200.87692416954[/C][C]-67.8769241695368[/C][/ROW]
[ROW][C]13[/C][C]9487[/C][C]9591.40771915248[/C][C]-104.407719152479[/C][/ROW]
[ROW][C]14[/C][C]8700[/C][C]8654.71989033148[/C][C]45.28010966852[/C][/ROW]
[ROW][C]15[/C][C]9627[/C][C]9560.03946696362[/C][C]66.9605330363752[/C][/ROW]
[ROW][C]16[/C][C]8947[/C][C]9224.88400154791[/C][C]-277.88400154791[/C][/ROW]
[ROW][C]17[/C][C]9283[/C][C]9380.19046998181[/C][C]-97.1904699818093[/C][/ROW]
[ROW][C]18[/C][C]8829[/C][C]9283.88677638959[/C][C]-454.886776389594[/C][/ROW]
[ROW][C]19[/C][C]9947[/C][C]9756.12912883447[/C][C]190.870871165534[/C][/ROW]
[ROW][C]20[/C][C]9628[/C][C]9664.41470316836[/C][C]-36.414703168355[/C][/ROW]
[ROW][C]21[/C][C]9318[/C][C]9416.23018835444[/C][C]-98.230188354443[/C][/ROW]
[ROW][C]22[/C][C]9605[/C][C]9585.6673811861[/C][C]19.3326188139058[/C][/ROW]
[ROW][C]23[/C][C]8640[/C][C]8792.4691358855[/C][C]-152.469135885506[/C][/ROW]
[ROW][C]24[/C][C]9214[/C][C]9201.9579728728[/C][C]12.0420271272051[/C][/ROW]
[ROW][C]25[/C][C]9567[/C][C]9549.29910760005[/C][C]17.7008923999518[/C][/ROW]
[ROW][C]26[/C][C]8547[/C][C]8638.897381061[/C][C]-91.8973810609998[/C][/ROW]
[ROW][C]27[/C][C]9185[/C][C]9639.56393812742[/C][C]-454.563938127416[/C][/ROW]
[ROW][C]28[/C][C]9470[/C][C]9219.28058491148[/C][C]250.719415088517[/C][/ROW]
[ROW][C]29[/C][C]9123[/C][C]9380.90705346195[/C][C]-257.907053461947[/C][/ROW]
[ROW][C]30[/C][C]9278[/C][C]9313.3976508723[/C][C]-35.3976508723052[/C][/ROW]
[ROW][C]31[/C][C]10170[/C][C]9983.09020764064[/C][C]186.909792359361[/C][/ROW]
[ROW][C]32[/C][C]9434[/C][C]9803.81818869323[/C][C]-369.818188693225[/C][/ROW]
[ROW][C]33[/C][C]9655[/C][C]9616.70151950468[/C][C]38.2984804953184[/C][/ROW]
[ROW][C]34[/C][C]9429[/C][C]9711.65520960319[/C][C]-282.655209603187[/C][/ROW]
[ROW][C]35[/C][C]8739[/C][C]8848.02307400035[/C][C]-109.023074000348[/C][/ROW]
[ROW][C]36[/C][C]9552[/C][C]9330.60658452209[/C][C]221.393415477912[/C][/ROW]
[ROW][C]37[/C][C]9687[/C][C]9628.88612966969[/C][C]58.1138703303142[/C][/ROW]
[ROW][C]38[/C][C]9019[/C][C]8811.0022884502[/C][C]207.997711549807[/C][/ROW]
[ROW][C]39[/C][C]9672[/C][C]9877.05329282463[/C][C]-205.053292824627[/C][/ROW]
[ROW][C]40[/C][C]9206[/C][C]9472.04251675742[/C][C]-266.042516757423[/C][/ROW]
[ROW][C]41[/C][C]9069[/C][C]9631.58416333172[/C][C]-562.58416333172[/C][/ROW]
[ROW][C]42[/C][C]9788[/C][C]9443.9771523542[/C][C]344.022847645803[/C][/ROW]
[ROW][C]43[/C][C]10312[/C][C]10031.0148461154[/C][C]280.98515388463[/C][/ROW]
[ROW][C]44[/C][C]10105[/C][C]9972.99839337021[/C][C]132.001606629791[/C][/ROW]
[ROW][C]45[/C][C]9863[/C][C]9930.2359877542[/C][C]-67.2359877542048[/C][/ROW]
[ROW][C]46[/C][C]9656[/C][C]9973.87510096127[/C][C]-317.875100961272[/C][/ROW]
[ROW][C]47[/C][C]9295[/C][C]9159.38031726819[/C][C]135.619682731807[/C][/ROW]
[ROW][C]48[/C][C]9946[/C][C]9566.2617597199[/C][C]379.738240280092[/C][/ROW]
[ROW][C]49[/C][C]9701[/C][C]9914.76024504909[/C][C]-213.760245049086[/C][/ROW]
[ROW][C]50[/C][C]9049[/C][C]9101.55455227325[/C][C]-52.554552273252[/C][/ROW]
[ROW][C]51[/C][C]10190[/C][C]10050.1080442064[/C][C]139.891955793584[/C][/ROW]
[ROW][C]52[/C][C]9706[/C][C]9611.58828331938[/C][C]94.4117166806154[/C][/ROW]
[ROW][C]53[/C][C]9765[/C][C]9871.49775171964[/C][C]-106.497751719637[/C][/ROW]
[ROW][C]54[/C][C]9893[/C][C]9835.15452834595[/C][C]57.8454716540549[/C][/ROW]
[ROW][C]55[/C][C]9994[/C][C]10379.600995394[/C][C]-385.600995393981[/C][/ROW]
[ROW][C]56[/C][C]10433[/C][C]10198.8309629785[/C][C]234.169037021484[/C][/ROW]
[ROW][C]57[/C][C]10073[/C][C]9998.77076821949[/C][C]74.2292317805112[/C][/ROW]
[ROW][C]58[/C][C]10112[/C][C]10045.3143279499[/C][C]66.6856720501176[/C][/ROW]
[ROW][C]59[/C][C]9266[/C][C]9410.29018913498[/C][C]-144.290189134976[/C][/ROW]
[ROW][C]60[/C][C]9820[/C][C]9772.06777951748[/C][C]47.9322204825245[/C][/ROW]
[ROW][C]61[/C][C]10097[/C][C]10075.1304587605[/C][C]21.8695412395087[/C][/ROW]
[ROW][C]62[/C][C]9115[/C][C]9182.25551864958[/C][C]-67.2555186495819[/C][/ROW]
[ROW][C]63[/C][C]10411[/C][C]10167.2628185727[/C][C]243.737181427304[/C][/ROW]
[ROW][C]64[/C][C]9678[/C][C]9820.67526047764[/C][C]-142.675260477637[/C][/ROW]
[ROW][C]65[/C][C]10408[/C][C]9991.87278178719[/C][C]416.127218212808[/C][/ROW]
[ROW][C]66[/C][C]10153[/C][C]10034.1797798627[/C][C]118.820220137337[/C][/ROW]
[ROW][C]67[/C][C]10368[/C][C]10597.9584482466[/C][C]-229.958448246608[/C][/ROW]
[ROW][C]68[/C][C]10581[/C][C]10532.5147193236[/C][C]48.4852806763519[/C][/ROW]
[ROW][C]69[/C][C]10597[/C][C]10224.4438132577[/C][C]372.556186742286[/C][/ROW]
[ROW][C]70[/C][C]10680[/C][C]10304.7967099857[/C][C]375.203290014327[/C][/ROW]
[ROW][C]71[/C][C]9738[/C][C]9691.5672333122[/C][C]46.4327666877956[/C][/ROW]
[ROW][C]72[/C][C]9556[/C][C]10149.2289791982[/C][C]-593.228979198197[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148791&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148791&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
197439522.51633976821220.48366023179
285878628.5703692345-41.5703692344929
397319521.97243930522209.02756069478
495639221.52935298616341.470647013838
599989389.9477797177608.052220282305
694379467.4041121753-30.4041121752958
71003810081.2063737689-43.2063737689353
899189926.42303246605-8.42303246604665
992529571.61772290947-319.617722909468
1097379597.69127031389139.30872968611
1190358811.27005039877223.729949601227
1291339200.87692416954-67.8769241695368
1394879591.40771915248-104.407719152479
1487008654.7198903314845.28010966852
1596279560.0394669636266.9605330363752
1689479224.88400154791-277.88400154791
1792839380.19046998181-97.1904699818093
1888299283.88677638959-454.886776389594
1999479756.12912883447190.870871165534
2096289664.41470316836-36.414703168355
2193189416.23018835444-98.230188354443
2296059585.667381186119.3326188139058
2386408792.4691358855-152.469135885506
2492149201.957972872812.0420271272051
2595679549.2991076000517.7008923999518
2685478638.897381061-91.8973810609998
2791859639.56393812742-454.563938127416
2894709219.28058491148250.719415088517
2991239380.90705346195-257.907053461947
3092789313.3976508723-35.3976508723052
31101709983.09020764064186.909792359361
3294349803.81818869323-369.818188693225
3396559616.7015195046838.2984804953184
3494299711.65520960319-282.655209603187
3587398848.02307400035-109.023074000348
3695529330.60658452209221.393415477912
3796879628.8861296696958.1138703303142
3890198811.0022884502207.997711549807
3996729877.05329282463-205.053292824627
4092069472.04251675742-266.042516757423
4190699631.58416333172-562.58416333172
4297889443.9771523542344.022847645803
431031210031.0148461154280.98515388463
44101059972.99839337021132.001606629791
4598639930.2359877542-67.2359877542048
4696569973.87510096127-317.875100961272
4792959159.38031726819135.619682731807
4899469566.2617597199379.738240280092
4997019914.76024504909-213.760245049086
5090499101.55455227325-52.554552273252
511019010050.1080442064139.891955793584
5297069611.5882833193894.4117166806154
5397659871.49775171964-106.497751719637
5498939835.1545283459557.8454716540549
55999410379.600995394-385.600995393981
561043310198.8309629785234.169037021484
57100739998.7707682194974.2292317805112
581011210045.314327949966.6856720501176
5992669410.29018913498-144.290189134976
6098209772.0677795174847.9322204825245
611009710075.130458760521.8695412395087
6291159182.25551864958-67.2555186495819
631041110167.2628185727243.737181427304
6496789820.67526047764-142.675260477637
65104089991.87278178719416.127218212808
661015310034.1797798627118.820220137337
671036810597.9584482466-229.958448246608
681058110532.514719323648.4852806763519
691059710224.4438132577372.556186742286
701068010304.7967099857375.203290014327
7197389691.567233312246.4327666877956
72955610149.2289791982-593.228979198197







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.7242996478483540.5514007043032920.275700352151646
200.638018728966440.7239625420671190.361981271033559
210.5762434137763780.8475131724472440.423756586223622
220.468246369568690.936492739137380.53175363043131
230.3771517988888260.7543035977776520.622848201111174
240.4560714869196320.9121429738392640.543928513080368
250.4029166911743630.8058333823487260.597083308825637
260.316443032043290.632886064086580.68355696795671
270.282430381833860.564860763667720.71756961816614
280.5428545025999680.9142909948000650.457145497400032
290.4999872540053590.9999745080107190.500012745994641
300.4457816473962180.8915632947924360.554218352603782
310.6793760319468220.6412479361063570.320623968053178
320.7154441832585590.5691116334828830.284555816741441
330.7381578944039870.5236842111920270.261842105596013
340.6705675789493220.6588648421013570.329432421050678
350.6938316413606280.6123367172787440.306168358639372
360.7147343043645030.5705313912709930.285265695635496
370.6725455200106820.6549089599786360.327454479989318
380.685205951650280.6295880966994420.314794048349721
390.6082902175835660.7834195648328680.391709782416434
400.5526765059191040.8946469881617930.447323494080896
410.7828173920734950.434365215853010.217182607926505
420.8261913777303680.3476172445392640.173808622269632
430.810940497621130.3781190047577410.18905950237887
440.8052589679741770.3894820640516460.194741032025823
450.7407072863006820.5185854273986350.259292713699317
460.6756923397282530.6486153205434940.324307660271747
470.5883276789018590.8233446421962820.411672321098141
480.7809079548744640.4381840902510710.219092045125536
490.6950575289699570.6098849420600870.304942471030043
500.7004410507624110.5991178984751780.299558949237589
510.7213533359551720.5572933280896560.278646664044828
520.7720657929311630.4558684141376740.227934207068837
530.6292870796234460.7414258407531090.370712920376554

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 & 0.724299647848354 & 0.551400704303292 & 0.275700352151646 \tabularnewline
20 & 0.63801872896644 & 0.723962542067119 & 0.361981271033559 \tabularnewline
21 & 0.576243413776378 & 0.847513172447244 & 0.423756586223622 \tabularnewline
22 & 0.46824636956869 & 0.93649273913738 & 0.53175363043131 \tabularnewline
23 & 0.377151798888826 & 0.754303597777652 & 0.622848201111174 \tabularnewline
24 & 0.456071486919632 & 0.912142973839264 & 0.543928513080368 \tabularnewline
25 & 0.402916691174363 & 0.805833382348726 & 0.597083308825637 \tabularnewline
26 & 0.31644303204329 & 0.63288606408658 & 0.68355696795671 \tabularnewline
27 & 0.28243038183386 & 0.56486076366772 & 0.71756961816614 \tabularnewline
28 & 0.542854502599968 & 0.914290994800065 & 0.457145497400032 \tabularnewline
29 & 0.499987254005359 & 0.999974508010719 & 0.500012745994641 \tabularnewline
30 & 0.445781647396218 & 0.891563294792436 & 0.554218352603782 \tabularnewline
31 & 0.679376031946822 & 0.641247936106357 & 0.320623968053178 \tabularnewline
32 & 0.715444183258559 & 0.569111633482883 & 0.284555816741441 \tabularnewline
33 & 0.738157894403987 & 0.523684211192027 & 0.261842105596013 \tabularnewline
34 & 0.670567578949322 & 0.658864842101357 & 0.329432421050678 \tabularnewline
35 & 0.693831641360628 & 0.612336717278744 & 0.306168358639372 \tabularnewline
36 & 0.714734304364503 & 0.570531391270993 & 0.285265695635496 \tabularnewline
37 & 0.672545520010682 & 0.654908959978636 & 0.327454479989318 \tabularnewline
38 & 0.68520595165028 & 0.629588096699442 & 0.314794048349721 \tabularnewline
39 & 0.608290217583566 & 0.783419564832868 & 0.391709782416434 \tabularnewline
40 & 0.552676505919104 & 0.894646988161793 & 0.447323494080896 \tabularnewline
41 & 0.782817392073495 & 0.43436521585301 & 0.217182607926505 \tabularnewline
42 & 0.826191377730368 & 0.347617244539264 & 0.173808622269632 \tabularnewline
43 & 0.81094049762113 & 0.378119004757741 & 0.18905950237887 \tabularnewline
44 & 0.805258967974177 & 0.389482064051646 & 0.194741032025823 \tabularnewline
45 & 0.740707286300682 & 0.518585427398635 & 0.259292713699317 \tabularnewline
46 & 0.675692339728253 & 0.648615320543494 & 0.324307660271747 \tabularnewline
47 & 0.588327678901859 & 0.823344642196282 & 0.411672321098141 \tabularnewline
48 & 0.780907954874464 & 0.438184090251071 & 0.219092045125536 \tabularnewline
49 & 0.695057528969957 & 0.609884942060087 & 0.304942471030043 \tabularnewline
50 & 0.700441050762411 & 0.599117898475178 & 0.299558949237589 \tabularnewline
51 & 0.721353335955172 & 0.557293328089656 & 0.278646664044828 \tabularnewline
52 & 0.772065792931163 & 0.455868414137674 & 0.227934207068837 \tabularnewline
53 & 0.629287079623446 & 0.741425840753109 & 0.370712920376554 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148791&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]19[/C][C]0.724299647848354[/C][C]0.551400704303292[/C][C]0.275700352151646[/C][/ROW]
[ROW][C]20[/C][C]0.63801872896644[/C][C]0.723962542067119[/C][C]0.361981271033559[/C][/ROW]
[ROW][C]21[/C][C]0.576243413776378[/C][C]0.847513172447244[/C][C]0.423756586223622[/C][/ROW]
[ROW][C]22[/C][C]0.46824636956869[/C][C]0.93649273913738[/C][C]0.53175363043131[/C][/ROW]
[ROW][C]23[/C][C]0.377151798888826[/C][C]0.754303597777652[/C][C]0.622848201111174[/C][/ROW]
[ROW][C]24[/C][C]0.456071486919632[/C][C]0.912142973839264[/C][C]0.543928513080368[/C][/ROW]
[ROW][C]25[/C][C]0.402916691174363[/C][C]0.805833382348726[/C][C]0.597083308825637[/C][/ROW]
[ROW][C]26[/C][C]0.31644303204329[/C][C]0.63288606408658[/C][C]0.68355696795671[/C][/ROW]
[ROW][C]27[/C][C]0.28243038183386[/C][C]0.56486076366772[/C][C]0.71756961816614[/C][/ROW]
[ROW][C]28[/C][C]0.542854502599968[/C][C]0.914290994800065[/C][C]0.457145497400032[/C][/ROW]
[ROW][C]29[/C][C]0.499987254005359[/C][C]0.999974508010719[/C][C]0.500012745994641[/C][/ROW]
[ROW][C]30[/C][C]0.445781647396218[/C][C]0.891563294792436[/C][C]0.554218352603782[/C][/ROW]
[ROW][C]31[/C][C]0.679376031946822[/C][C]0.641247936106357[/C][C]0.320623968053178[/C][/ROW]
[ROW][C]32[/C][C]0.715444183258559[/C][C]0.569111633482883[/C][C]0.284555816741441[/C][/ROW]
[ROW][C]33[/C][C]0.738157894403987[/C][C]0.523684211192027[/C][C]0.261842105596013[/C][/ROW]
[ROW][C]34[/C][C]0.670567578949322[/C][C]0.658864842101357[/C][C]0.329432421050678[/C][/ROW]
[ROW][C]35[/C][C]0.693831641360628[/C][C]0.612336717278744[/C][C]0.306168358639372[/C][/ROW]
[ROW][C]36[/C][C]0.714734304364503[/C][C]0.570531391270993[/C][C]0.285265695635496[/C][/ROW]
[ROW][C]37[/C][C]0.672545520010682[/C][C]0.654908959978636[/C][C]0.327454479989318[/C][/ROW]
[ROW][C]38[/C][C]0.68520595165028[/C][C]0.629588096699442[/C][C]0.314794048349721[/C][/ROW]
[ROW][C]39[/C][C]0.608290217583566[/C][C]0.783419564832868[/C][C]0.391709782416434[/C][/ROW]
[ROW][C]40[/C][C]0.552676505919104[/C][C]0.894646988161793[/C][C]0.447323494080896[/C][/ROW]
[ROW][C]41[/C][C]0.782817392073495[/C][C]0.43436521585301[/C][C]0.217182607926505[/C][/ROW]
[ROW][C]42[/C][C]0.826191377730368[/C][C]0.347617244539264[/C][C]0.173808622269632[/C][/ROW]
[ROW][C]43[/C][C]0.81094049762113[/C][C]0.378119004757741[/C][C]0.18905950237887[/C][/ROW]
[ROW][C]44[/C][C]0.805258967974177[/C][C]0.389482064051646[/C][C]0.194741032025823[/C][/ROW]
[ROW][C]45[/C][C]0.740707286300682[/C][C]0.518585427398635[/C][C]0.259292713699317[/C][/ROW]
[ROW][C]46[/C][C]0.675692339728253[/C][C]0.648615320543494[/C][C]0.324307660271747[/C][/ROW]
[ROW][C]47[/C][C]0.588327678901859[/C][C]0.823344642196282[/C][C]0.411672321098141[/C][/ROW]
[ROW][C]48[/C][C]0.780907954874464[/C][C]0.438184090251071[/C][C]0.219092045125536[/C][/ROW]
[ROW][C]49[/C][C]0.695057528969957[/C][C]0.609884942060087[/C][C]0.304942471030043[/C][/ROW]
[ROW][C]50[/C][C]0.700441050762411[/C][C]0.599117898475178[/C][C]0.299558949237589[/C][/ROW]
[ROW][C]51[/C][C]0.721353335955172[/C][C]0.557293328089656[/C][C]0.278646664044828[/C][/ROW]
[ROW][C]52[/C][C]0.772065792931163[/C][C]0.455868414137674[/C][C]0.227934207068837[/C][/ROW]
[ROW][C]53[/C][C]0.629287079623446[/C][C]0.741425840753109[/C][C]0.370712920376554[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148791&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148791&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
190.7242996478483540.5514007043032920.275700352151646
200.638018728966440.7239625420671190.361981271033559
210.5762434137763780.8475131724472440.423756586223622
220.468246369568690.936492739137380.53175363043131
230.3771517988888260.7543035977776520.622848201111174
240.4560714869196320.9121429738392640.543928513080368
250.4029166911743630.8058333823487260.597083308825637
260.316443032043290.632886064086580.68355696795671
270.282430381833860.564860763667720.71756961816614
280.5428545025999680.9142909948000650.457145497400032
290.4999872540053590.9999745080107190.500012745994641
300.4457816473962180.8915632947924360.554218352603782
310.6793760319468220.6412479361063570.320623968053178
320.7154441832585590.5691116334828830.284555816741441
330.7381578944039870.5236842111920270.261842105596013
340.6705675789493220.6588648421013570.329432421050678
350.6938316413606280.6123367172787440.306168358639372
360.7147343043645030.5705313912709930.285265695635496
370.6725455200106820.6549089599786360.327454479989318
380.685205951650280.6295880966994420.314794048349721
390.6082902175835660.7834195648328680.391709782416434
400.5526765059191040.8946469881617930.447323494080896
410.7828173920734950.434365215853010.217182607926505
420.8261913777303680.3476172445392640.173808622269632
430.810940497621130.3781190047577410.18905950237887
440.8052589679741770.3894820640516460.194741032025823
450.7407072863006820.5185854273986350.259292713699317
460.6756923397282530.6486153205434940.324307660271747
470.5883276789018590.8233446421962820.411672321098141
480.7809079548744640.4381840902510710.219092045125536
490.6950575289699570.6098849420600870.304942471030043
500.7004410507624110.5991178984751780.299558949237589
510.7213533359551720.5572933280896560.278646664044828
520.7720657929311630.4558684141376740.227934207068837
530.6292870796234460.7414258407531090.370712920376554







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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148791&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148791&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148791&T=6

As an alternative you can also use a QR Code:  

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

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



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')
}