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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, 30 Nov 2010 16:17:19 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/30/t1291133793vf1bf9lzc7hghbd.htm/, Retrieved Thu, 31 Oct 2024 23:27:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=103657, Retrieved Thu, 31 Oct 2024 23:27:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact236
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [Aantal reizigers ...] [2010-11-26 01:03:33] [97ad38b1c3b35a5feca8b85f7bc7b3ff]
-    D      [Multiple Regression] [Aantal reizigers ...] [2010-11-26 01:44:20] [97ad38b1c3b35a5feca8b85f7bc7b3ff]
-    D        [Multiple Regression] [Aantal reizigers ...] [2010-11-26 01:59:36] [97ad38b1c3b35a5feca8b85f7bc7b3ff]
-   PD          [Multiple Regression] [Regressiemodel] [2010-11-28 16:11:23] [39c51da0be01189e8a44eb69e891b7a1]
F   PD              [Multiple Regression] [autoregression wi...] [2010-11-30 16:17:19] [6e52d1bada9435d33ddf990b22ee4b00] [Current]
Feedback Forum
2010-12-03 11:02:16 [Stefanie Van Esbroeck] [reply
Ook bij dit model maakte je een correcte berekening en een goed en uitgebreide conclusie. Ook hier kan ik weinig tot zelfs geen feedback aan toevoegen dan enkel te zeggen van goed gewerkt.

Post a new message
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 time7 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 7 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103657&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103657&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103657&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 time7 seconds
R Server'George Udny Yule' @ 72.249.76.132







Multiple Linear Regression - Estimated Regression Equation
Yt[t] = + 3540.69197220561 + 0.130932028247944`Yt-1`[t] + 0.201818490077343`Yt-2`[t] + 0.264823448542267`Yt-3`[t] + 385.773255960307M1[t] -436.30006975701M2[t] + 469.50316191611M3[t] + 72.8939494758238M4[t] + 333.401103073757M5[t] + 79.6860766985089M6[t] + 718.477094565068M7[t] + 477.862169357032M8[t] + 160.8783375403M9[t] + 134.048534247597M10[t] -554.848198896826M11[t] + 5.16340774563452t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Yt[t] =  +  3540.69197220561 +  0.130932028247944`Yt-1`[t] +  0.201818490077343`Yt-2`[t] +  0.264823448542267`Yt-3`[t] +  385.773255960307M1[t] -436.30006975701M2[t] +  469.50316191611M3[t] +  72.8939494758238M4[t] +  333.401103073757M5[t] +  79.6860766985089M6[t] +  718.477094565068M7[t] +  477.862169357032M8[t] +  160.8783375403M9[t] +  134.048534247597M10[t] -554.848198896826M11[t] +  5.16340774563452t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103657&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Yt[t] =  +  3540.69197220561 +  0.130932028247944`Yt-1`[t] +  0.201818490077343`Yt-2`[t] +  0.264823448542267`Yt-3`[t] +  385.773255960307M1[t] -436.30006975701M2[t] +  469.50316191611M3[t] +  72.8939494758238M4[t] +  333.401103073757M5[t] +  79.6860766985089M6[t] +  718.477094565068M7[t] +  477.862169357032M8[t] +  160.8783375403M9[t] +  134.048534247597M10[t] -554.848198896826M11[t] +  5.16340774563452t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103657&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103657&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] = + 3540.69197220561 + 0.130932028247944`Yt-1`[t] + 0.201818490077343`Yt-2`[t] + 0.264823448542267`Yt-3`[t] + 385.773255960307M1[t] -436.30006975701M2[t] + 469.50316191611M3[t] + 72.8939494758238M4[t] + 333.401103073757M5[t] + 79.6860766985089M6[t] + 718.477094565068M7[t] + 477.862169357032M8[t] + 160.8783375403M9[t] + 134.048534247597M10[t] -554.848198896826M11[t] + 5.16340774563452t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3540.691972205611487.8911512.37970.0207590.01038
`Yt-1`0.1309320282479440.1339030.97780.3323710.166186
`Yt-2`0.2018184900773430.1290491.56390.1234760.061738
`Yt-3`0.2648234485422670.1342511.97260.0534860.026743
M1385.773255960307200.1878081.92710.0590520.029526
M2-436.30006975701220.764862-1.97630.0530530.026527
M3469.50316191611159.0440282.9520.0046050.002303
M472.8939494758238235.720340.30920.7582870.379144
M5333.401103073757212.066041.57220.1215480.060774
M679.6860766985089183.3585010.43460.6655290.332764
M7718.477094565068182.2649613.94190.0002270.000113
M8477.862169357032222.7425832.14540.0362750.018138
M9160.8783375403205.8738380.78140.4378340.218917
M10134.048534247597177.7129110.75430.453830.226915
M11-554.848198896826184.793507-3.00250.0039960.001998
t5.163407745634522.4226492.13130.0374670.018733

\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) & 3540.69197220561 & 1487.891151 & 2.3797 & 0.020759 & 0.01038 \tabularnewline
`Yt-1` & 0.130932028247944 & 0.133903 & 0.9778 & 0.332371 & 0.166186 \tabularnewline
`Yt-2` & 0.201818490077343 & 0.129049 & 1.5639 & 0.123476 & 0.061738 \tabularnewline
`Yt-3` & 0.264823448542267 & 0.134251 & 1.9726 & 0.053486 & 0.026743 \tabularnewline
M1 & 385.773255960307 & 200.187808 & 1.9271 & 0.059052 & 0.029526 \tabularnewline
M2 & -436.30006975701 & 220.764862 & -1.9763 & 0.053053 & 0.026527 \tabularnewline
M3 & 469.50316191611 & 159.044028 & 2.952 & 0.004605 & 0.002303 \tabularnewline
M4 & 72.8939494758238 & 235.72034 & 0.3092 & 0.758287 & 0.379144 \tabularnewline
M5 & 333.401103073757 & 212.06604 & 1.5722 & 0.121548 & 0.060774 \tabularnewline
M6 & 79.6860766985089 & 183.358501 & 0.4346 & 0.665529 & 0.332764 \tabularnewline
M7 & 718.477094565068 & 182.264961 & 3.9419 & 0.000227 & 0.000113 \tabularnewline
M8 & 477.862169357032 & 222.742583 & 2.1454 & 0.036275 & 0.018138 \tabularnewline
M9 & 160.8783375403 & 205.873838 & 0.7814 & 0.437834 & 0.218917 \tabularnewline
M10 & 134.048534247597 & 177.712911 & 0.7543 & 0.45383 & 0.226915 \tabularnewline
M11 & -554.848198896826 & 184.793507 & -3.0025 & 0.003996 & 0.001998 \tabularnewline
t & 5.16340774563452 & 2.422649 & 2.1313 & 0.037467 & 0.018733 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103657&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]3540.69197220561[/C][C]1487.891151[/C][C]2.3797[/C][C]0.020759[/C][C]0.01038[/C][/ROW]
[ROW][C]`Yt-1`[/C][C]0.130932028247944[/C][C]0.133903[/C][C]0.9778[/C][C]0.332371[/C][C]0.166186[/C][/ROW]
[ROW][C]`Yt-2`[/C][C]0.201818490077343[/C][C]0.129049[/C][C]1.5639[/C][C]0.123476[/C][C]0.061738[/C][/ROW]
[ROW][C]`Yt-3`[/C][C]0.264823448542267[/C][C]0.134251[/C][C]1.9726[/C][C]0.053486[/C][C]0.026743[/C][/ROW]
[ROW][C]M1[/C][C]385.773255960307[/C][C]200.187808[/C][C]1.9271[/C][C]0.059052[/C][C]0.029526[/C][/ROW]
[ROW][C]M2[/C][C]-436.30006975701[/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]72.8939494758238[/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]79.6860766985089[/C][C]183.358501[/C][C]0.4346[/C][C]0.665529[/C][C]0.332764[/C][/ROW]
[ROW][C]M7[/C][C]718.477094565068[/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]160.8783375403[/C][C]205.873838[/C][C]0.7814[/C][C]0.437834[/C][C]0.218917[/C][/ROW]
[ROW][C]M10[/C][C]134.048534247597[/C][C]177.712911[/C][C]0.7543[/C][C]0.45383[/C][C]0.226915[/C][/ROW]
[ROW][C]M11[/C][C]-554.848198896826[/C][C]184.793507[/C][C]-3.0025[/C][C]0.003996[/C][C]0.001998[/C][/ROW]
[ROW][C]t[/C][C]5.16340774563452[/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=103657&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103657&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)3540.691972205611487.8911512.37970.0207590.01038
`Yt-1`0.1309320282479440.1339030.97780.3323710.166186
`Yt-2`0.2018184900773430.1290491.56390.1234760.061738
`Yt-3`0.2648234485422670.1342511.97260.0534860.026743
M1385.773255960307200.1878081.92710.0590520.029526
M2-436.30006975701220.764862-1.97630.0530530.026527
M3469.50316191611159.0440282.9520.0046050.002303
M472.8939494758238235.720340.30920.7582870.379144
M5333.401103073757212.066041.57220.1215480.060774
M679.6860766985089183.3585010.43460.6655290.332764
M7718.477094565068182.2649613.94190.0002270.000113
M8477.862169357032222.7425832.14540.0362750.018138
M9160.8783375403205.8738380.78140.4378340.218917
M10134.048534247597177.7129110.75430.453830.226915
M11-554.848198896826184.793507-3.00250.0039960.001998
t5.163407745634522.4226492.13130.0374670.018733







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 Deviation268.914224668373
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 & 268.914224668373 \tabularnewline
Sum Squared Residuals & 4049632.17282355 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103657&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]268.914224668373[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]4049632.17282355[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103657&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103657&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 Deviation268.914224668373
Sum Squared Residuals4049632.17282355







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
197439522.5163397682220.483660231804
285878628.5703692345-41.5703692344936
397319521.97243930522209.027560694778
495639221.52935298616341.470647013836
599989389.9477797177608.052220282304
694379467.4041121753-30.4041121752984
71003810081.2063737689-43.2063737689387
899189926.42303246605-8.42303246604785
992529571.61772290947-319.617722909469
1097379597.6912703139139.308729686109
1190358811.27005039877223.729949601227
1291339200.87692416954-67.8769241695381
1394879591.40771915248-104.407719152483
1487008654.7198903314845.2801096685198
1596279560.0394669636366.9605330363746
1689479224.88400154791-277.884001547911
1792839380.19046998181-97.1904699818096
1888299283.88677638959-454.886776389593
1999479756.12912883447190.870871165534
2096289664.41470316835-36.4147031683539
2193189416.23018835444-98.230188354443
2296059585.667381186119.3326188139060
2386408792.4691358855-152.469135885506
2492149201.957972872812.0420271272052
2595679549.2991076000517.7008923999494
2685478638.897381061-91.8973810609994
2791859639.56393812742-454.563938127415
2894709219.28058491148250.719415088518
2991239380.90705346195-257.907053461946
3092789313.3976508723-35.3976508723051
31101709983.09020764064186.909792359363
3294349803.81818869322-369.818188693224
3396559616.7015195046838.2984804953184
3494299711.65520960319-282.655209603186
3587398848.02307400035-109.023074000347
3695529330.60658452209221.393415477913
3796879628.8861296696958.1138703303121
3890198811.0022884502207.997711549807
3996729877.05329282463-205.053292824626
4092069472.04251675742-266.042516757423
4190699631.58416333172-562.584163331719
4297889443.9771523542344.022847645804
431031210031.0148461154280.985153884632
44101059972.99839337021132.001606629791
4598639930.2359877542-67.2359877542051
4696569973.87510096127-317.875100961272
4792959159.38031726819135.619682731807
4899469566.2617597199379.738240280093
4997019914.7602450491-213.760245049090
5090499101.55455227325-52.5545522732525
511019010050.1080442064139.891955793585
5297069611.5882833193894.4117166806158
5397659871.49775171964-106.497751719637
5498939835.1545283459557.845471654055
55999410379.6009953940-385.600995393981
561043310198.8309629785234.169037021484
57100739998.7707682194974.2292317805122
581011210045.314327949966.6856720501176
5992669410.29018913498-144.290189134975
6098209772.0677795174847.9322204825248
611009710075.130458760521.8695412395063
6291159182.25551864958-67.2555186495814
631041110167.2628185727243.737181427304
6496789820.67526047764-142.675260477636
65104089991.87278178719416.127218212808
661015310034.1797798627118.820220137337
671036810597.9584482466-229.958448246609
681058110532.514719323648.4852806763516
691059710224.4438132577372.556186742286
701068010304.7967099857375.203290014326
7197389691.567233312246.4327666877946
72955610149.2289791982-593.228979198198

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 9743 & 9522.5163397682 & 220.483660231804 \tabularnewline
2 & 8587 & 8628.5703692345 & -41.5703692344936 \tabularnewline
3 & 9731 & 9521.97243930522 & 209.027560694778 \tabularnewline
4 & 9563 & 9221.52935298616 & 341.470647013836 \tabularnewline
5 & 9998 & 9389.9477797177 & 608.052220282304 \tabularnewline
6 & 9437 & 9467.4041121753 & -30.4041121752984 \tabularnewline
7 & 10038 & 10081.2063737689 & -43.2063737689387 \tabularnewline
8 & 9918 & 9926.42303246605 & -8.42303246604785 \tabularnewline
9 & 9252 & 9571.61772290947 & -319.617722909469 \tabularnewline
10 & 9737 & 9597.6912703139 & 139.308729686109 \tabularnewline
11 & 9035 & 8811.27005039877 & 223.729949601227 \tabularnewline
12 & 9133 & 9200.87692416954 & -67.8769241695381 \tabularnewline
13 & 9487 & 9591.40771915248 & -104.407719152483 \tabularnewline
14 & 8700 & 8654.71989033148 & 45.2801096685198 \tabularnewline
15 & 9627 & 9560.03946696363 & 66.9605330363746 \tabularnewline
16 & 8947 & 9224.88400154791 & -277.884001547911 \tabularnewline
17 & 9283 & 9380.19046998181 & -97.1904699818096 \tabularnewline
18 & 8829 & 9283.88677638959 & -454.886776389593 \tabularnewline
19 & 9947 & 9756.12912883447 & 190.870871165534 \tabularnewline
20 & 9628 & 9664.41470316835 & -36.4147031683539 \tabularnewline
21 & 9318 & 9416.23018835444 & -98.230188354443 \tabularnewline
22 & 9605 & 9585.6673811861 & 19.3326188139060 \tabularnewline
23 & 8640 & 8792.4691358855 & -152.469135885506 \tabularnewline
24 & 9214 & 9201.9579728728 & 12.0420271272052 \tabularnewline
25 & 9567 & 9549.29910760005 & 17.7008923999494 \tabularnewline
26 & 8547 & 8638.897381061 & -91.8973810609994 \tabularnewline
27 & 9185 & 9639.56393812742 & -454.563938127415 \tabularnewline
28 & 9470 & 9219.28058491148 & 250.719415088518 \tabularnewline
29 & 9123 & 9380.90705346195 & -257.907053461946 \tabularnewline
30 & 9278 & 9313.3976508723 & -35.3976508723051 \tabularnewline
31 & 10170 & 9983.09020764064 & 186.909792359363 \tabularnewline
32 & 9434 & 9803.81818869322 & -369.818188693224 \tabularnewline
33 & 9655 & 9616.70151950468 & 38.2984804953184 \tabularnewline
34 & 9429 & 9711.65520960319 & -282.655209603186 \tabularnewline
35 & 8739 & 8848.02307400035 & -109.023074000347 \tabularnewline
36 & 9552 & 9330.60658452209 & 221.393415477913 \tabularnewline
37 & 9687 & 9628.88612966969 & 58.1138703303121 \tabularnewline
38 & 9019 & 8811.0022884502 & 207.997711549807 \tabularnewline
39 & 9672 & 9877.05329282463 & -205.053292824626 \tabularnewline
40 & 9206 & 9472.04251675742 & -266.042516757423 \tabularnewline
41 & 9069 & 9631.58416333172 & -562.584163331719 \tabularnewline
42 & 9788 & 9443.9771523542 & 344.022847645804 \tabularnewline
43 & 10312 & 10031.0148461154 & 280.985153884632 \tabularnewline
44 & 10105 & 9972.99839337021 & 132.001606629791 \tabularnewline
45 & 9863 & 9930.2359877542 & -67.2359877542051 \tabularnewline
46 & 9656 & 9973.87510096127 & -317.875100961272 \tabularnewline
47 & 9295 & 9159.38031726819 & 135.619682731807 \tabularnewline
48 & 9946 & 9566.2617597199 & 379.738240280093 \tabularnewline
49 & 9701 & 9914.7602450491 & -213.760245049090 \tabularnewline
50 & 9049 & 9101.55455227325 & -52.5545522732525 \tabularnewline
51 & 10190 & 10050.1080442064 & 139.891955793585 \tabularnewline
52 & 9706 & 9611.58828331938 & 94.4117166806158 \tabularnewline
53 & 9765 & 9871.49775171964 & -106.497751719637 \tabularnewline
54 & 9893 & 9835.15452834595 & 57.845471654055 \tabularnewline
55 & 9994 & 10379.6009953940 & -385.600995393981 \tabularnewline
56 & 10433 & 10198.8309629785 & 234.169037021484 \tabularnewline
57 & 10073 & 9998.77076821949 & 74.2292317805122 \tabularnewline
58 & 10112 & 10045.3143279499 & 66.6856720501176 \tabularnewline
59 & 9266 & 9410.29018913498 & -144.290189134975 \tabularnewline
60 & 9820 & 9772.06777951748 & 47.9322204825248 \tabularnewline
61 & 10097 & 10075.1304587605 & 21.8695412395063 \tabularnewline
62 & 9115 & 9182.25551864958 & -67.2555186495814 \tabularnewline
63 & 10411 & 10167.2628185727 & 243.737181427304 \tabularnewline
64 & 9678 & 9820.67526047764 & -142.675260477636 \tabularnewline
65 & 10408 & 9991.87278178719 & 416.127218212808 \tabularnewline
66 & 10153 & 10034.1797798627 & 118.820220137337 \tabularnewline
67 & 10368 & 10597.9584482466 & -229.958448246609 \tabularnewline
68 & 10581 & 10532.5147193236 & 48.4852806763516 \tabularnewline
69 & 10597 & 10224.4438132577 & 372.556186742286 \tabularnewline
70 & 10680 & 10304.7967099857 & 375.203290014326 \tabularnewline
71 & 9738 & 9691.5672333122 & 46.4327666877946 \tabularnewline
72 & 9556 & 10149.2289791982 & -593.228979198198 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103657&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]9522.5163397682[/C][C]220.483660231804[/C][/ROW]
[ROW][C]2[/C][C]8587[/C][C]8628.5703692345[/C][C]-41.5703692344936[/C][/ROW]
[ROW][C]3[/C][C]9731[/C][C]9521.97243930522[/C][C]209.027560694778[/C][/ROW]
[ROW][C]4[/C][C]9563[/C][C]9221.52935298616[/C][C]341.470647013836[/C][/ROW]
[ROW][C]5[/C][C]9998[/C][C]9389.9477797177[/C][C]608.052220282304[/C][/ROW]
[ROW][C]6[/C][C]9437[/C][C]9467.4041121753[/C][C]-30.4041121752984[/C][/ROW]
[ROW][C]7[/C][C]10038[/C][C]10081.2063737689[/C][C]-43.2063737689387[/C][/ROW]
[ROW][C]8[/C][C]9918[/C][C]9926.42303246605[/C][C]-8.42303246604785[/C][/ROW]
[ROW][C]9[/C][C]9252[/C][C]9571.61772290947[/C][C]-319.617722909469[/C][/ROW]
[ROW][C]10[/C][C]9737[/C][C]9597.6912703139[/C][C]139.308729686109[/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]-67.8769241695381[/C][/ROW]
[ROW][C]13[/C][C]9487[/C][C]9591.40771915248[/C][C]-104.407719152483[/C][/ROW]
[ROW][C]14[/C][C]8700[/C][C]8654.71989033148[/C][C]45.2801096685198[/C][/ROW]
[ROW][C]15[/C][C]9627[/C][C]9560.03946696363[/C][C]66.9605330363746[/C][/ROW]
[ROW][C]16[/C][C]8947[/C][C]9224.88400154791[/C][C]-277.884001547911[/C][/ROW]
[ROW][C]17[/C][C]9283[/C][C]9380.19046998181[/C][C]-97.1904699818096[/C][/ROW]
[ROW][C]18[/C][C]8829[/C][C]9283.88677638959[/C][C]-454.886776389593[/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]9664.41470316835[/C][C]-36.4147031683539[/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]19.3326188139060[/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]12.0420271272052[/C][/ROW]
[ROW][C]25[/C][C]9567[/C][C]9549.29910760005[/C][C]17.7008923999494[/C][/ROW]
[ROW][C]26[/C][C]8547[/C][C]8638.897381061[/C][C]-91.8973810609994[/C][/ROW]
[ROW][C]27[/C][C]9185[/C][C]9639.56393812742[/C][C]-454.563938127415[/C][/ROW]
[ROW][C]28[/C][C]9470[/C][C]9219.28058491148[/C][C]250.719415088518[/C][/ROW]
[ROW][C]29[/C][C]9123[/C][C]9380.90705346195[/C][C]-257.907053461946[/C][/ROW]
[ROW][C]30[/C][C]9278[/C][C]9313.3976508723[/C][C]-35.3976508723051[/C][/ROW]
[ROW][C]31[/C][C]10170[/C][C]9983.09020764064[/C][C]186.909792359363[/C][/ROW]
[ROW][C]32[/C][C]9434[/C][C]9803.81818869322[/C][C]-369.818188693224[/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]-282.655209603186[/C][/ROW]
[ROW][C]35[/C][C]8739[/C][C]8848.02307400035[/C][C]-109.023074000347[/C][/ROW]
[ROW][C]36[/C][C]9552[/C][C]9330.60658452209[/C][C]221.393415477913[/C][/ROW]
[ROW][C]37[/C][C]9687[/C][C]9628.88612966969[/C][C]58.1138703303121[/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]-205.053292824626[/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]-562.584163331719[/C][/ROW]
[ROW][C]42[/C][C]9788[/C][C]9443.9771523542[/C][C]344.022847645804[/C][/ROW]
[ROW][C]43[/C][C]10312[/C][C]10031.0148461154[/C][C]280.985153884632[/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]-67.2359877542051[/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]379.738240280093[/C][/ROW]
[ROW][C]49[/C][C]9701[/C][C]9914.7602450491[/C][C]-213.760245049090[/C][/ROW]
[ROW][C]50[/C][C]9049[/C][C]9101.55455227325[/C][C]-52.5545522732525[/C][/ROW]
[ROW][C]51[/C][C]10190[/C][C]10050.1080442064[/C][C]139.891955793585[/C][/ROW]
[ROW][C]52[/C][C]9706[/C][C]9611.58828331938[/C][C]94.4117166806158[/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]57.845471654055[/C][/ROW]
[ROW][C]55[/C][C]9994[/C][C]10379.6009953940[/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]74.2292317805122[/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]-144.290189134975[/C][/ROW]
[ROW][C]60[/C][C]9820[/C][C]9772.06777951748[/C][C]47.9322204825248[/C][/ROW]
[ROW][C]61[/C][C]10097[/C][C]10075.1304587605[/C][C]21.8695412395063[/C][/ROW]
[ROW][C]62[/C][C]9115[/C][C]9182.25551864958[/C][C]-67.2555186495814[/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]-142.675260477636[/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]-229.958448246609[/C][/ROW]
[ROW][C]68[/C][C]10581[/C][C]10532.5147193236[/C][C]48.4852806763516[/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]375.203290014326[/C][/ROW]
[ROW][C]71[/C][C]9738[/C][C]9691.5672333122[/C][C]46.4327666877946[/C][/ROW]
[ROW][C]72[/C][C]9556[/C][C]10149.2289791982[/C][C]-593.228979198198[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103657&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103657&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
197439522.5163397682220.483660231804
285878628.5703692345-41.5703692344936
397319521.97243930522209.027560694778
495639221.52935298616341.470647013836
599989389.9477797177608.052220282304
694379467.4041121753-30.4041121752984
71003810081.2063737689-43.2063737689387
899189926.42303246605-8.42303246604785
992529571.61772290947-319.617722909469
1097379597.6912703139139.308729686109
1190358811.27005039877223.729949601227
1291339200.87692416954-67.8769241695381
1394879591.40771915248-104.407719152483
1487008654.7198903314845.2801096685198
1596279560.0394669636366.9605330363746
1689479224.88400154791-277.884001547911
1792839380.19046998181-97.1904699818096
1888299283.88677638959-454.886776389593
1999479756.12912883447190.870871165534
2096289664.41470316835-36.4147031683539
2193189416.23018835444-98.230188354443
2296059585.667381186119.3326188139060
2386408792.4691358855-152.469135885506
2492149201.957972872812.0420271272052
2595679549.2991076000517.7008923999494
2685478638.897381061-91.8973810609994
2791859639.56393812742-454.563938127415
2894709219.28058491148250.719415088518
2991239380.90705346195-257.907053461946
3092789313.3976508723-35.3976508723051
31101709983.09020764064186.909792359363
3294349803.81818869322-369.818188693224
3396559616.7015195046838.2984804953184
3494299711.65520960319-282.655209603186
3587398848.02307400035-109.023074000347
3695529330.60658452209221.393415477913
3796879628.8861296696958.1138703303121
3890198811.0022884502207.997711549807
3996729877.05329282463-205.053292824626
4092069472.04251675742-266.042516757423
4190699631.58416333172-562.584163331719
4297889443.9771523542344.022847645804
431031210031.0148461154280.985153884632
44101059972.99839337021132.001606629791
4598639930.2359877542-67.2359877542051
4696569973.87510096127-317.875100961272
4792959159.38031726819135.619682731807
4899469566.2617597199379.738240280093
4997019914.7602450491-213.760245049090
5090499101.55455227325-52.5545522732525
511019010050.1080442064139.891955793585
5297069611.5882833193894.4117166806158
5397659871.49775171964-106.497751719637
5498939835.1545283459557.845471654055
55999410379.6009953940-385.600995393981
561043310198.8309629785234.169037021484
57100739998.7707682194974.2292317805122
581011210045.314327949966.6856720501176
5992669410.29018913498-144.290189134975
6098209772.0677795174847.9322204825248
611009710075.130458760521.8695412395063
6291159182.25551864958-67.2555186495814
631041110167.2628185727243.737181427304
6496789820.67526047764-142.675260477636
65104089991.87278178719416.127218212808
661015310034.1797798627118.820220137337
671036810597.9584482466-229.958448246609
681058110532.514719323648.4852806763516
691059710224.4438132577372.556186742286
701068010304.7967099857375.203290014326
7197389691.567233312246.4327666877946
72955610149.2289791982-593.228979198198







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.7242996478483510.5514007043032970.275700352151649
200.638018728966440.7239625420671190.361981271033559
210.5762434137763850.847513172447230.423756586223615
220.468246369568690.936492739137380.53175363043131
230.3771517988888240.7543035977776480.622848201111176
240.4560714869196270.9121429738392540.543928513080373
250.4029166911743630.8058333823487250.597083308825637
260.316443032043290.632886064086580.68355696795671
270.2824303818338600.5648607636677210.71756961816614
280.5428545025999660.9142909948000680.457145497400034
290.4999872540053590.9999745080107190.500012745994641
300.4457816473962380.8915632947924770.554218352603762
310.6793760319468320.6412479361063360.320623968053168
320.7154441832585590.5691116334828830.284555816741441
330.7381578944039840.5236842111920320.261842105596016
340.6705675789493180.6588648421013640.329432421050682
350.693831641360630.612336717278740.30616835863937
360.7147343043645040.5705313912709920.285265695635496
370.6725455200106820.6549089599786350.327454479989317
380.6852059516502740.6295880966994530.314794048349726
390.6082902175835660.7834195648328680.391709782416434
400.5526765059190950.894646988161810.447323494080905
410.7828173920734970.4343652158530070.217182607926503
420.8261913777303640.3476172445392720.173808622269636
430.8109404976211260.3781190047577470.189059502378874
440.8052589679741780.3894820640516440.194741032025822
450.7407072863006870.5185854273986260.259292713699313
460.675692339728250.6486153205434990.324307660271750
470.5883276789018770.8233446421962460.411672321098123
480.7809079548744670.4381840902510650.219092045125533
490.6950575289699510.6098849420600980.304942471030049
500.7004410507624170.5991178984751670.299558949237583
510.7213533359551710.5572933280896580.278646664044829
520.7720657929311680.4558684141376630.227934207068832
530.6292870796234630.7414258407530740.370712920376537

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 & 0.724299647848351 & 0.551400704303297 & 0.275700352151649 \tabularnewline
20 & 0.63801872896644 & 0.723962542067119 & 0.361981271033559 \tabularnewline
21 & 0.576243413776385 & 0.84751317244723 & 0.423756586223615 \tabularnewline
22 & 0.46824636956869 & 0.93649273913738 & 0.53175363043131 \tabularnewline
23 & 0.377151798888824 & 0.754303597777648 & 0.622848201111176 \tabularnewline
24 & 0.456071486919627 & 0.912142973839254 & 0.543928513080373 \tabularnewline
25 & 0.402916691174363 & 0.805833382348725 & 0.597083308825637 \tabularnewline
26 & 0.31644303204329 & 0.63288606408658 & 0.68355696795671 \tabularnewline
27 & 0.282430381833860 & 0.564860763667721 & 0.71756961816614 \tabularnewline
28 & 0.542854502599966 & 0.914290994800068 & 0.457145497400034 \tabularnewline
29 & 0.499987254005359 & 0.999974508010719 & 0.500012745994641 \tabularnewline
30 & 0.445781647396238 & 0.891563294792477 & 0.554218352603762 \tabularnewline
31 & 0.679376031946832 & 0.641247936106336 & 0.320623968053168 \tabularnewline
32 & 0.715444183258559 & 0.569111633482883 & 0.284555816741441 \tabularnewline
33 & 0.738157894403984 & 0.523684211192032 & 0.261842105596016 \tabularnewline
34 & 0.670567578949318 & 0.658864842101364 & 0.329432421050682 \tabularnewline
35 & 0.69383164136063 & 0.61233671727874 & 0.30616835863937 \tabularnewline
36 & 0.714734304364504 & 0.570531391270992 & 0.285265695635496 \tabularnewline
37 & 0.672545520010682 & 0.654908959978635 & 0.327454479989317 \tabularnewline
38 & 0.685205951650274 & 0.629588096699453 & 0.314794048349726 \tabularnewline
39 & 0.608290217583566 & 0.783419564832868 & 0.391709782416434 \tabularnewline
40 & 0.552676505919095 & 0.89464698816181 & 0.447323494080905 \tabularnewline
41 & 0.782817392073497 & 0.434365215853007 & 0.217182607926503 \tabularnewline
42 & 0.826191377730364 & 0.347617244539272 & 0.173808622269636 \tabularnewline
43 & 0.810940497621126 & 0.378119004757747 & 0.189059502378874 \tabularnewline
44 & 0.805258967974178 & 0.389482064051644 & 0.194741032025822 \tabularnewline
45 & 0.740707286300687 & 0.518585427398626 & 0.259292713699313 \tabularnewline
46 & 0.67569233972825 & 0.648615320543499 & 0.324307660271750 \tabularnewline
47 & 0.588327678901877 & 0.823344642196246 & 0.411672321098123 \tabularnewline
48 & 0.780907954874467 & 0.438184090251065 & 0.219092045125533 \tabularnewline
49 & 0.695057528969951 & 0.609884942060098 & 0.304942471030049 \tabularnewline
50 & 0.700441050762417 & 0.599117898475167 & 0.299558949237583 \tabularnewline
51 & 0.721353335955171 & 0.557293328089658 & 0.278646664044829 \tabularnewline
52 & 0.772065792931168 & 0.455868414137663 & 0.227934207068832 \tabularnewline
53 & 0.629287079623463 & 0.741425840753074 & 0.370712920376537 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103657&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]0.724299647848351[/C][C]0.551400704303297[/C][C]0.275700352151649[/C][/ROW]
[ROW][C]20[/C][C]0.63801872896644[/C][C]0.723962542067119[/C][C]0.361981271033559[/C][/ROW]
[ROW][C]21[/C][C]0.576243413776385[/C][C]0.84751317244723[/C][C]0.423756586223615[/C][/ROW]
[ROW][C]22[/C][C]0.46824636956869[/C][C]0.93649273913738[/C][C]0.53175363043131[/C][/ROW]
[ROW][C]23[/C][C]0.377151798888824[/C][C]0.754303597777648[/C][C]0.622848201111176[/C][/ROW]
[ROW][C]24[/C][C]0.456071486919627[/C][C]0.912142973839254[/C][C]0.543928513080373[/C][/ROW]
[ROW][C]25[/C][C]0.402916691174363[/C][C]0.805833382348725[/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]0.282430381833860[/C][C]0.564860763667721[/C][C]0.71756961816614[/C][/ROW]
[ROW][C]28[/C][C]0.542854502599966[/C][C]0.914290994800068[/C][C]0.457145497400034[/C][/ROW]
[ROW][C]29[/C][C]0.499987254005359[/C][C]0.999974508010719[/C][C]0.500012745994641[/C][/ROW]
[ROW][C]30[/C][C]0.445781647396238[/C][C]0.891563294792477[/C][C]0.554218352603762[/C][/ROW]
[ROW][C]31[/C][C]0.679376031946832[/C][C]0.641247936106336[/C][C]0.320623968053168[/C][/ROW]
[ROW][C]32[/C][C]0.715444183258559[/C][C]0.569111633482883[/C][C]0.284555816741441[/C][/ROW]
[ROW][C]33[/C][C]0.738157894403984[/C][C]0.523684211192032[/C][C]0.261842105596016[/C][/ROW]
[ROW][C]34[/C][C]0.670567578949318[/C][C]0.658864842101364[/C][C]0.329432421050682[/C][/ROW]
[ROW][C]35[/C][C]0.69383164136063[/C][C]0.61233671727874[/C][C]0.30616835863937[/C][/ROW]
[ROW][C]36[/C][C]0.714734304364504[/C][C]0.570531391270992[/C][C]0.285265695635496[/C][/ROW]
[ROW][C]37[/C][C]0.672545520010682[/C][C]0.654908959978635[/C][C]0.327454479989317[/C][/ROW]
[ROW][C]38[/C][C]0.685205951650274[/C][C]0.629588096699453[/C][C]0.314794048349726[/C][/ROW]
[ROW][C]39[/C][C]0.608290217583566[/C][C]0.783419564832868[/C][C]0.391709782416434[/C][/ROW]
[ROW][C]40[/C][C]0.552676505919095[/C][C]0.89464698816181[/C][C]0.447323494080905[/C][/ROW]
[ROW][C]41[/C][C]0.782817392073497[/C][C]0.434365215853007[/C][C]0.217182607926503[/C][/ROW]
[ROW][C]42[/C][C]0.826191377730364[/C][C]0.347617244539272[/C][C]0.173808622269636[/C][/ROW]
[ROW][C]43[/C][C]0.810940497621126[/C][C]0.378119004757747[/C][C]0.189059502378874[/C][/ROW]
[ROW][C]44[/C][C]0.805258967974178[/C][C]0.389482064051644[/C][C]0.194741032025822[/C][/ROW]
[ROW][C]45[/C][C]0.740707286300687[/C][C]0.518585427398626[/C][C]0.259292713699313[/C][/ROW]
[ROW][C]46[/C][C]0.67569233972825[/C][C]0.648615320543499[/C][C]0.324307660271750[/C][/ROW]
[ROW][C]47[/C][C]0.588327678901877[/C][C]0.823344642196246[/C][C]0.411672321098123[/C][/ROW]
[ROW][C]48[/C][C]0.780907954874467[/C][C]0.438184090251065[/C][C]0.219092045125533[/C][/ROW]
[ROW][C]49[/C][C]0.695057528969951[/C][C]0.609884942060098[/C][C]0.304942471030049[/C][/ROW]
[ROW][C]50[/C][C]0.700441050762417[/C][C]0.599117898475167[/C][C]0.299558949237583[/C][/ROW]
[ROW][C]51[/C][C]0.721353335955171[/C][C]0.557293328089658[/C][C]0.278646664044829[/C][/ROW]
[ROW][C]52[/C][C]0.772065792931168[/C][C]0.455868414137663[/C][C]0.227934207068832[/C][/ROW]
[ROW][C]53[/C][C]0.629287079623463[/C][C]0.741425840753074[/C][C]0.370712920376537[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103657&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103657&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
190.7242996478483510.5514007043032970.275700352151649
200.638018728966440.7239625420671190.361981271033559
210.5762434137763850.847513172447230.423756586223615
220.468246369568690.936492739137380.53175363043131
230.3771517988888240.7543035977776480.622848201111176
240.4560714869196270.9121429738392540.543928513080373
250.4029166911743630.8058333823487250.597083308825637
260.316443032043290.632886064086580.68355696795671
270.2824303818338600.5648607636677210.71756961816614
280.5428545025999660.9142909948000680.457145497400034
290.4999872540053590.9999745080107190.500012745994641
300.4457816473962380.8915632947924770.554218352603762
310.6793760319468320.6412479361063360.320623968053168
320.7154441832585590.5691116334828830.284555816741441
330.7381578944039840.5236842111920320.261842105596016
340.6705675789493180.6588648421013640.329432421050682
350.693831641360630.612336717278740.30616835863937
360.7147343043645040.5705313912709920.285265695635496
370.6725455200106820.6549089599786350.327454479989317
380.6852059516502740.6295880966994530.314794048349726
390.6082902175835660.7834195648328680.391709782416434
400.5526765059190950.894646988161810.447323494080905
410.7828173920734970.4343652158530070.217182607926503
420.8261913777303640.3476172445392720.173808622269636
430.8109404976211260.3781190047577470.189059502378874
440.8052589679741780.3894820640516440.194741032025822
450.7407072863006870.5185854273986260.259292713699313
460.675692339728250.6486153205434990.324307660271750
470.5883276789018770.8233446421962460.411672321098123
480.7809079548744670.4381840902510650.219092045125533
490.6950575289699510.6098849420600980.304942471030049
500.7004410507624170.5991178984751670.299558949237583
510.7213533359551710.5572933280896580.278646664044829
520.7720657929311680.4558684141376630.227934207068832
530.6292870796234630.7414258407530740.370712920376537







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=103657&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=103657&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103657&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')
}