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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 19 Nov 2009 09:26:03 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/19/t12586480123eudsjbu1mw117b.htm/, Retrieved Thu, 28 Mar 2024 12:22:39 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57807, Retrieved Thu, 28 Mar 2024 12:22:39 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact125
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 14:03:14] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [workshop 7 bereke...] [2009-11-19 16:26:03] [78d370e6d5f4594e9982a5085e7604c6] [Current]
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Dataseries X:
4716.99	0
4926.65	0
4920.10	0
5170.09	0
5246.24	0
5283.61	0
4979.05	0
4825.20	0
4695.12	0
4711.54	0
4727.22	0
4384.96	0
4378.75	0
4472.93	0
4564.07	0
4310.54	0
4171.38	0
4049.38	0
3591.37	0
3720.46	0
4107.23	0
4101.71	0
4162.34	0
4136.22	0
4125.88	0
4031.48	0
3761.36	0
3408.56	0
3228.47	0
3090.45	0
2741.14	0
2980.44	0
3104.33	0
3181.57	0
2863.86	0
2898.01	0
3112.33	0
3254.33	0
3513.47	0
3587.61	0
3727.45	0
3793.34	0
3817.58	0
3845.13	0
3931.86	0
4197.52	0
4307.13	0
4229.43	0
4362.28	0
4217.34	0
4361.28	0
4327.74	0
4417.65	0
4557.68	0
4650.35	0
4967.18	0
5123.42	0
5290.85	0
5535.66	0
5514.06	0
5493.88	0
5694.83	0
5850.41	0
6116.64	0
6175.00	0
6513.58	0
6383.78	0
6673.66	0
6936.61	0
7300.68	0
7392.93	0
7497.31	0
7584.71	0
7160.79	0
7196.19	0
7245.63	0
7347.51	0
7425.75	0
7778.51	0
7822.33	0
8181.22	0
8371.47	0
8347.71	0
8672.11	0
8802.79	0
9138.46	0
9123.29	0
9023.21	1
8850.41	1
8864.58	1
9163.74	1
8516.66	1
8553.44	1
7555.20	1
7851.22	1
7442.00	1
7992.53	1
8264.04	1
7517.39	1
7200.40	1
7193.69	1
6193.58	1
5104.21	1
4800.46	1
4461.61	1
4398.59	1
4243.63	1
4293.82	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57807&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57807&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57807&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 5025.8413037037 + 1917.67413333333X[t] + 379.988237037039M1[t] + 445.622681481482M2[t] + 406.368237037036M3[t] + 146.944444444444M4[t] + 143.320000000000M5[t] + 78.2255555555556M6[t] -95.3544444444446M7[t] -101.822222222222M8[t] + 2.99111111111107M9[t] + 4.57888888888867M10[t] + 40.4199999999999M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  5025.8413037037 +  1917.67413333333X[t] +  379.988237037039M1[t] +  445.622681481482M2[t] +  406.368237037036M3[t] +  146.944444444444M4[t] +  143.320000000000M5[t] +  78.2255555555556M6[t] -95.3544444444446M7[t] -101.822222222222M8[t] +  2.99111111111107M9[t] +  4.57888888888867M10[t] +  40.4199999999999M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57807&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  5025.8413037037 +  1917.67413333333X[t] +  379.988237037039M1[t] +  445.622681481482M2[t] +  406.368237037036M3[t] +  146.944444444444M4[t] +  143.320000000000M5[t] +  78.2255555555556M6[t] -95.3544444444446M7[t] -101.822222222222M8[t] +  2.99111111111107M9[t] +  4.57888888888867M10[t] +  40.4199999999999M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57807&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57807&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 5025.8413037037 + 1917.67413333333X[t] + 379.988237037039M1[t] + 445.622681481482M2[t] + 406.368237037036M3[t] + 146.944444444444M4[t] + 143.320000000000M5[t] + 78.2255555555556M6[t] -95.3544444444446M7[t] -101.822222222222M8[t] + 2.99111111111107M9[t] + 4.57888888888867M10[t] + 40.4199999999999M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5025.8413037037602.1852578.34600
X1917.67413333333436.7292384.3912.9e-051.5e-05
M1379.988237037039841.8854550.45140.6527630.326382
M2445.622681481482841.8854550.52930.5978220.298911
M3406.368237037036841.8854550.48270.6304280.315214
M4146.944444444444840.485810.17480.8615830.430792
M5143.320000000000840.485810.17050.8649640.432482
M678.2255555555556840.485810.09310.9260420.463021
M7-95.3544444444446840.48581-0.11350.9099120.454956
M8-101.822222222222840.48581-0.12110.9038310.451915
M92.99111111111107840.485810.00360.9971680.498584
M104.57888888888867840.485810.00540.9956650.497832
M1140.4199999999999840.485810.04810.9617440.480872

\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) & 5025.8413037037 & 602.185257 & 8.346 & 0 & 0 \tabularnewline
X & 1917.67413333333 & 436.729238 & 4.391 & 2.9e-05 & 1.5e-05 \tabularnewline
M1 & 379.988237037039 & 841.885455 & 0.4514 & 0.652763 & 0.326382 \tabularnewline
M2 & 445.622681481482 & 841.885455 & 0.5293 & 0.597822 & 0.298911 \tabularnewline
M3 & 406.368237037036 & 841.885455 & 0.4827 & 0.630428 & 0.315214 \tabularnewline
M4 & 146.944444444444 & 840.48581 & 0.1748 & 0.861583 & 0.430792 \tabularnewline
M5 & 143.320000000000 & 840.48581 & 0.1705 & 0.864964 & 0.432482 \tabularnewline
M6 & 78.2255555555556 & 840.48581 & 0.0931 & 0.926042 & 0.463021 \tabularnewline
M7 & -95.3544444444446 & 840.48581 & -0.1135 & 0.909912 & 0.454956 \tabularnewline
M8 & -101.822222222222 & 840.48581 & -0.1211 & 0.903831 & 0.451915 \tabularnewline
M9 & 2.99111111111107 & 840.48581 & 0.0036 & 0.997168 & 0.498584 \tabularnewline
M10 & 4.57888888888867 & 840.48581 & 0.0054 & 0.995665 & 0.497832 \tabularnewline
M11 & 40.4199999999999 & 840.48581 & 0.0481 & 0.961744 & 0.480872 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57807&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]5025.8413037037[/C][C]602.185257[/C][C]8.346[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]1917.67413333333[/C][C]436.729238[/C][C]4.391[/C][C]2.9e-05[/C][C]1.5e-05[/C][/ROW]
[ROW][C]M1[/C][C]379.988237037039[/C][C]841.885455[/C][C]0.4514[/C][C]0.652763[/C][C]0.326382[/C][/ROW]
[ROW][C]M2[/C][C]445.622681481482[/C][C]841.885455[/C][C]0.5293[/C][C]0.597822[/C][C]0.298911[/C][/ROW]
[ROW][C]M3[/C][C]406.368237037036[/C][C]841.885455[/C][C]0.4827[/C][C]0.630428[/C][C]0.315214[/C][/ROW]
[ROW][C]M4[/C][C]146.944444444444[/C][C]840.48581[/C][C]0.1748[/C][C]0.861583[/C][C]0.430792[/C][/ROW]
[ROW][C]M5[/C][C]143.320000000000[/C][C]840.48581[/C][C]0.1705[/C][C]0.864964[/C][C]0.432482[/C][/ROW]
[ROW][C]M6[/C][C]78.2255555555556[/C][C]840.48581[/C][C]0.0931[/C][C]0.926042[/C][C]0.463021[/C][/ROW]
[ROW][C]M7[/C][C]-95.3544444444446[/C][C]840.48581[/C][C]-0.1135[/C][C]0.909912[/C][C]0.454956[/C][/ROW]
[ROW][C]M8[/C][C]-101.822222222222[/C][C]840.48581[/C][C]-0.1211[/C][C]0.903831[/C][C]0.451915[/C][/ROW]
[ROW][C]M9[/C][C]2.99111111111107[/C][C]840.48581[/C][C]0.0036[/C][C]0.997168[/C][C]0.498584[/C][/ROW]
[ROW][C]M10[/C][C]4.57888888888867[/C][C]840.48581[/C][C]0.0054[/C][C]0.995665[/C][C]0.497832[/C][/ROW]
[ROW][C]M11[/C][C]40.4199999999999[/C][C]840.48581[/C][C]0.0481[/C][C]0.961744[/C][C]0.480872[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57807&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57807&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)5025.8413037037602.1852578.34600
X1917.67413333333436.7292384.3912.9e-051.5e-05
M1379.988237037039841.8854550.45140.6527630.326382
M2445.622681481482841.8854550.52930.5978220.298911
M3406.368237037036841.8854550.48270.6304280.315214
M4146.944444444444840.485810.17480.8615830.430792
M5143.320000000000840.485810.17050.8649640.432482
M678.2255555555556840.485810.09310.9260420.463021
M7-95.3544444444446840.48581-0.11350.9099120.454956
M8-101.822222222222840.48581-0.12110.9038310.451915
M92.99111111111107840.485810.00360.9971680.498584
M104.57888888888867840.485810.00540.9956650.497832
M1140.4199999999999840.485810.04810.9617440.480872







Multiple Linear Regression - Regression Statistics
Multiple R0.414076228513992
R-squared0.171459123020372
Adjusted R-squared0.0668013280334715
F-TEST (value)1.63828335043589
F-TEST (DF numerator)12
F-TEST (DF denominator)95
p-value0.0938966040374087
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1782.93964770604
Sum Squared Residuals301993009.799404

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.414076228513992 \tabularnewline
R-squared & 0.171459123020372 \tabularnewline
Adjusted R-squared & 0.0668013280334715 \tabularnewline
F-TEST (value) & 1.63828335043589 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 95 \tabularnewline
p-value & 0.0938966040374087 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1782.93964770604 \tabularnewline
Sum Squared Residuals & 301993009.799404 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57807&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.414076228513992[/C][/ROW]
[ROW][C]R-squared[/C][C]0.171459123020372[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0668013280334715[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.63828335043589[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]95[/C][/ROW]
[ROW][C]p-value[/C][C]0.0938966040374087[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1782.93964770604[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]301993009.799404[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57807&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57807&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 R0.414076228513992
R-squared0.171459123020372
Adjusted R-squared0.0668013280334715
F-TEST (value)1.63828335043589
F-TEST (DF numerator)12
F-TEST (DF denominator)95
p-value0.0938966040374087
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1782.93964770604
Sum Squared Residuals301993009.799404







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14716.995405.82954074073-688.839540740731
24926.655471.46398518519-544.813985185187
34920.15432.20954074074-512.109540740741
45170.095172.78574814815-2.69574814814985
55246.245169.161303703777.0786962962968
65283.615104.06685925926179.54314074074
74979.054930.4868592592648.5631407407415
84825.24924.01908148148-98.819081481482
94695.125028.83241481481-333.712414814815
104711.545030.42019259259-318.880192592593
114727.225066.2613037037-339.041303703704
124384.965025.8413037037-640.881303703704
134378.755405.82954074074-1027.07954074074
144472.935471.46398518519-998.533985185185
154564.075432.20954074074-868.139540740741
164310.545172.78574814815-862.245748148148
174171.385169.1613037037-997.781303703704
184049.385104.06685925926-1054.68685925926
193591.374930.48685925926-1339.11685925926
203720.464924.01908148148-1203.55908148148
214107.235028.83241481481-921.602414814815
224101.715030.42019259259-928.710192592593
234162.345066.2613037037-903.921303703704
244136.225025.8413037037-889.621303703704
254125.885405.82954074074-1279.94954074074
264031.485471.46398518519-1439.98398518519
273761.365432.20954074074-1670.84954074074
283408.565172.78574814815-1764.22574814815
293228.475169.1613037037-1940.69130370370
303090.455104.06685925926-2013.61685925926
312741.144930.48685925926-2189.34685925926
322980.444924.01908148148-1943.57908148148
333104.335028.83241481481-1924.50241481482
343181.575030.42019259259-1848.85019259259
352863.865066.2613037037-2202.40130370370
362898.015025.8413037037-2127.83130370370
373112.335405.82954074074-2293.49954074074
383254.335471.46398518518-2217.13398518518
393513.475432.20954074074-1918.73954074074
403587.615172.78574814815-1585.17574814815
413727.455169.1613037037-1441.71130370370
423793.345104.06685925926-1310.72685925926
433817.584930.48685925926-1112.90685925926
443845.134924.01908148148-1078.88908148148
453931.865028.83241481481-1096.97241481482
464197.525030.42019259259-832.900192592592
474307.135066.2613037037-759.131303703704
484229.435025.8413037037-796.411303703704
494362.285405.82954074074-1043.54954074074
504217.345471.46398518519-1254.12398518519
514361.285432.20954074074-1070.92954074074
524327.745172.78574814815-845.045748148149
534417.655169.1613037037-751.511303703704
544557.685104.06685925926-546.386859259259
554650.354930.48685925926-280.136859259259
564967.184924.0190814814843.1609185185189
575123.425028.8324148148194.5875851851853
585290.855030.42019259259260.429807407408
595535.665066.2613037037469.398696296296
605514.065025.8413037037488.218696296296
615493.885405.8295407407488.0504592592574
625694.835471.46398518519223.366014814815
635850.415432.20954074074418.200459259259
646116.645172.78574814815943.854251851852
6561755169.16130370371005.83869629630
666513.585104.066859259261409.51314074074
676383.784930.486859259261453.29314074074
686673.664924.019081481481749.64091851852
696936.615028.832414814811907.77758518518
707300.685030.420192592592270.25980740741
717392.935066.26130370372326.66869629630
727497.315025.84130370372471.46869629630
737584.715405.829540740742178.88045925926
747160.795471.463985185191689.32601481481
757196.195432.209540740741763.98045925926
767245.635172.785748148152072.84425185185
777347.515169.16130370372178.34869629630
787425.755104.066859259262321.68314074074
797778.514930.486859259262848.02314074074
807822.334924.019081481482898.31091851852
818181.225028.832414814813152.38758518519
828371.475030.420192592593341.04980740741
838347.715066.26130370373281.44869629630
848672.115025.84130370373646.26869629630
858802.795405.829540740743396.96045925926
869138.465471.463985185183666.99601481481
879123.295432.209540740743691.08045925926
889023.217090.459881481481932.75011851852
898850.417086.835437037041763.57456296296
908864.587021.740992592591842.83900740741
919163.746848.16099259262315.57900740741
928516.666841.693214814811674.96678518519
938553.446946.506548148151606.93345185185
947555.26948.09432592593607.105674074074
957851.226983.93543703704867.284562962964
9674426943.51543703704498.484562962963
977992.537323.50367407408669.026325925924
988264.047389.13811851852874.901881481483
997517.397349.88367407407167.506325925927
1007200.47090.45988148148109.940118518518
1017193.697086.83543703704106.854562962963
1026193.587021.74099259259-828.160992592592
1035104.216848.16099259259-1743.95099259259
1044800.466841.69321481482-2041.23321481482
1054461.616946.50654814815-2484.89654814815
1064398.596948.09432592593-2549.50432592593
1074243.636983.93543703704-2740.30543703704
1084293.826943.51543703704-2649.69543703704

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4716.99 & 5405.82954074073 & -688.839540740731 \tabularnewline
2 & 4926.65 & 5471.46398518519 & -544.813985185187 \tabularnewline
3 & 4920.1 & 5432.20954074074 & -512.109540740741 \tabularnewline
4 & 5170.09 & 5172.78574814815 & -2.69574814814985 \tabularnewline
5 & 5246.24 & 5169.1613037037 & 77.0786962962968 \tabularnewline
6 & 5283.61 & 5104.06685925926 & 179.54314074074 \tabularnewline
7 & 4979.05 & 4930.48685925926 & 48.5631407407415 \tabularnewline
8 & 4825.2 & 4924.01908148148 & -98.819081481482 \tabularnewline
9 & 4695.12 & 5028.83241481481 & -333.712414814815 \tabularnewline
10 & 4711.54 & 5030.42019259259 & -318.880192592593 \tabularnewline
11 & 4727.22 & 5066.2613037037 & -339.041303703704 \tabularnewline
12 & 4384.96 & 5025.8413037037 & -640.881303703704 \tabularnewline
13 & 4378.75 & 5405.82954074074 & -1027.07954074074 \tabularnewline
14 & 4472.93 & 5471.46398518519 & -998.533985185185 \tabularnewline
15 & 4564.07 & 5432.20954074074 & -868.139540740741 \tabularnewline
16 & 4310.54 & 5172.78574814815 & -862.245748148148 \tabularnewline
17 & 4171.38 & 5169.1613037037 & -997.781303703704 \tabularnewline
18 & 4049.38 & 5104.06685925926 & -1054.68685925926 \tabularnewline
19 & 3591.37 & 4930.48685925926 & -1339.11685925926 \tabularnewline
20 & 3720.46 & 4924.01908148148 & -1203.55908148148 \tabularnewline
21 & 4107.23 & 5028.83241481481 & -921.602414814815 \tabularnewline
22 & 4101.71 & 5030.42019259259 & -928.710192592593 \tabularnewline
23 & 4162.34 & 5066.2613037037 & -903.921303703704 \tabularnewline
24 & 4136.22 & 5025.8413037037 & -889.621303703704 \tabularnewline
25 & 4125.88 & 5405.82954074074 & -1279.94954074074 \tabularnewline
26 & 4031.48 & 5471.46398518519 & -1439.98398518519 \tabularnewline
27 & 3761.36 & 5432.20954074074 & -1670.84954074074 \tabularnewline
28 & 3408.56 & 5172.78574814815 & -1764.22574814815 \tabularnewline
29 & 3228.47 & 5169.1613037037 & -1940.69130370370 \tabularnewline
30 & 3090.45 & 5104.06685925926 & -2013.61685925926 \tabularnewline
31 & 2741.14 & 4930.48685925926 & -2189.34685925926 \tabularnewline
32 & 2980.44 & 4924.01908148148 & -1943.57908148148 \tabularnewline
33 & 3104.33 & 5028.83241481481 & -1924.50241481482 \tabularnewline
34 & 3181.57 & 5030.42019259259 & -1848.85019259259 \tabularnewline
35 & 2863.86 & 5066.2613037037 & -2202.40130370370 \tabularnewline
36 & 2898.01 & 5025.8413037037 & -2127.83130370370 \tabularnewline
37 & 3112.33 & 5405.82954074074 & -2293.49954074074 \tabularnewline
38 & 3254.33 & 5471.46398518518 & -2217.13398518518 \tabularnewline
39 & 3513.47 & 5432.20954074074 & -1918.73954074074 \tabularnewline
40 & 3587.61 & 5172.78574814815 & -1585.17574814815 \tabularnewline
41 & 3727.45 & 5169.1613037037 & -1441.71130370370 \tabularnewline
42 & 3793.34 & 5104.06685925926 & -1310.72685925926 \tabularnewline
43 & 3817.58 & 4930.48685925926 & -1112.90685925926 \tabularnewline
44 & 3845.13 & 4924.01908148148 & -1078.88908148148 \tabularnewline
45 & 3931.86 & 5028.83241481481 & -1096.97241481482 \tabularnewline
46 & 4197.52 & 5030.42019259259 & -832.900192592592 \tabularnewline
47 & 4307.13 & 5066.2613037037 & -759.131303703704 \tabularnewline
48 & 4229.43 & 5025.8413037037 & -796.411303703704 \tabularnewline
49 & 4362.28 & 5405.82954074074 & -1043.54954074074 \tabularnewline
50 & 4217.34 & 5471.46398518519 & -1254.12398518519 \tabularnewline
51 & 4361.28 & 5432.20954074074 & -1070.92954074074 \tabularnewline
52 & 4327.74 & 5172.78574814815 & -845.045748148149 \tabularnewline
53 & 4417.65 & 5169.1613037037 & -751.511303703704 \tabularnewline
54 & 4557.68 & 5104.06685925926 & -546.386859259259 \tabularnewline
55 & 4650.35 & 4930.48685925926 & -280.136859259259 \tabularnewline
56 & 4967.18 & 4924.01908148148 & 43.1609185185189 \tabularnewline
57 & 5123.42 & 5028.83241481481 & 94.5875851851853 \tabularnewline
58 & 5290.85 & 5030.42019259259 & 260.429807407408 \tabularnewline
59 & 5535.66 & 5066.2613037037 & 469.398696296296 \tabularnewline
60 & 5514.06 & 5025.8413037037 & 488.218696296296 \tabularnewline
61 & 5493.88 & 5405.82954074074 & 88.0504592592574 \tabularnewline
62 & 5694.83 & 5471.46398518519 & 223.366014814815 \tabularnewline
63 & 5850.41 & 5432.20954074074 & 418.200459259259 \tabularnewline
64 & 6116.64 & 5172.78574814815 & 943.854251851852 \tabularnewline
65 & 6175 & 5169.1613037037 & 1005.83869629630 \tabularnewline
66 & 6513.58 & 5104.06685925926 & 1409.51314074074 \tabularnewline
67 & 6383.78 & 4930.48685925926 & 1453.29314074074 \tabularnewline
68 & 6673.66 & 4924.01908148148 & 1749.64091851852 \tabularnewline
69 & 6936.61 & 5028.83241481481 & 1907.77758518518 \tabularnewline
70 & 7300.68 & 5030.42019259259 & 2270.25980740741 \tabularnewline
71 & 7392.93 & 5066.2613037037 & 2326.66869629630 \tabularnewline
72 & 7497.31 & 5025.8413037037 & 2471.46869629630 \tabularnewline
73 & 7584.71 & 5405.82954074074 & 2178.88045925926 \tabularnewline
74 & 7160.79 & 5471.46398518519 & 1689.32601481481 \tabularnewline
75 & 7196.19 & 5432.20954074074 & 1763.98045925926 \tabularnewline
76 & 7245.63 & 5172.78574814815 & 2072.84425185185 \tabularnewline
77 & 7347.51 & 5169.1613037037 & 2178.34869629630 \tabularnewline
78 & 7425.75 & 5104.06685925926 & 2321.68314074074 \tabularnewline
79 & 7778.51 & 4930.48685925926 & 2848.02314074074 \tabularnewline
80 & 7822.33 & 4924.01908148148 & 2898.31091851852 \tabularnewline
81 & 8181.22 & 5028.83241481481 & 3152.38758518519 \tabularnewline
82 & 8371.47 & 5030.42019259259 & 3341.04980740741 \tabularnewline
83 & 8347.71 & 5066.2613037037 & 3281.44869629630 \tabularnewline
84 & 8672.11 & 5025.8413037037 & 3646.26869629630 \tabularnewline
85 & 8802.79 & 5405.82954074074 & 3396.96045925926 \tabularnewline
86 & 9138.46 & 5471.46398518518 & 3666.99601481481 \tabularnewline
87 & 9123.29 & 5432.20954074074 & 3691.08045925926 \tabularnewline
88 & 9023.21 & 7090.45988148148 & 1932.75011851852 \tabularnewline
89 & 8850.41 & 7086.83543703704 & 1763.57456296296 \tabularnewline
90 & 8864.58 & 7021.74099259259 & 1842.83900740741 \tabularnewline
91 & 9163.74 & 6848.1609925926 & 2315.57900740741 \tabularnewline
92 & 8516.66 & 6841.69321481481 & 1674.96678518519 \tabularnewline
93 & 8553.44 & 6946.50654814815 & 1606.93345185185 \tabularnewline
94 & 7555.2 & 6948.09432592593 & 607.105674074074 \tabularnewline
95 & 7851.22 & 6983.93543703704 & 867.284562962964 \tabularnewline
96 & 7442 & 6943.51543703704 & 498.484562962963 \tabularnewline
97 & 7992.53 & 7323.50367407408 & 669.026325925924 \tabularnewline
98 & 8264.04 & 7389.13811851852 & 874.901881481483 \tabularnewline
99 & 7517.39 & 7349.88367407407 & 167.506325925927 \tabularnewline
100 & 7200.4 & 7090.45988148148 & 109.940118518518 \tabularnewline
101 & 7193.69 & 7086.83543703704 & 106.854562962963 \tabularnewline
102 & 6193.58 & 7021.74099259259 & -828.160992592592 \tabularnewline
103 & 5104.21 & 6848.16099259259 & -1743.95099259259 \tabularnewline
104 & 4800.46 & 6841.69321481482 & -2041.23321481482 \tabularnewline
105 & 4461.61 & 6946.50654814815 & -2484.89654814815 \tabularnewline
106 & 4398.59 & 6948.09432592593 & -2549.50432592593 \tabularnewline
107 & 4243.63 & 6983.93543703704 & -2740.30543703704 \tabularnewline
108 & 4293.82 & 6943.51543703704 & -2649.69543703704 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57807&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]4716.99[/C][C]5405.82954074073[/C][C]-688.839540740731[/C][/ROW]
[ROW][C]2[/C][C]4926.65[/C][C]5471.46398518519[/C][C]-544.813985185187[/C][/ROW]
[ROW][C]3[/C][C]4920.1[/C][C]5432.20954074074[/C][C]-512.109540740741[/C][/ROW]
[ROW][C]4[/C][C]5170.09[/C][C]5172.78574814815[/C][C]-2.69574814814985[/C][/ROW]
[ROW][C]5[/C][C]5246.24[/C][C]5169.1613037037[/C][C]77.0786962962968[/C][/ROW]
[ROW][C]6[/C][C]5283.61[/C][C]5104.06685925926[/C][C]179.54314074074[/C][/ROW]
[ROW][C]7[/C][C]4979.05[/C][C]4930.48685925926[/C][C]48.5631407407415[/C][/ROW]
[ROW][C]8[/C][C]4825.2[/C][C]4924.01908148148[/C][C]-98.819081481482[/C][/ROW]
[ROW][C]9[/C][C]4695.12[/C][C]5028.83241481481[/C][C]-333.712414814815[/C][/ROW]
[ROW][C]10[/C][C]4711.54[/C][C]5030.42019259259[/C][C]-318.880192592593[/C][/ROW]
[ROW][C]11[/C][C]4727.22[/C][C]5066.2613037037[/C][C]-339.041303703704[/C][/ROW]
[ROW][C]12[/C][C]4384.96[/C][C]5025.8413037037[/C][C]-640.881303703704[/C][/ROW]
[ROW][C]13[/C][C]4378.75[/C][C]5405.82954074074[/C][C]-1027.07954074074[/C][/ROW]
[ROW][C]14[/C][C]4472.93[/C][C]5471.46398518519[/C][C]-998.533985185185[/C][/ROW]
[ROW][C]15[/C][C]4564.07[/C][C]5432.20954074074[/C][C]-868.139540740741[/C][/ROW]
[ROW][C]16[/C][C]4310.54[/C][C]5172.78574814815[/C][C]-862.245748148148[/C][/ROW]
[ROW][C]17[/C][C]4171.38[/C][C]5169.1613037037[/C][C]-997.781303703704[/C][/ROW]
[ROW][C]18[/C][C]4049.38[/C][C]5104.06685925926[/C][C]-1054.68685925926[/C][/ROW]
[ROW][C]19[/C][C]3591.37[/C][C]4930.48685925926[/C][C]-1339.11685925926[/C][/ROW]
[ROW][C]20[/C][C]3720.46[/C][C]4924.01908148148[/C][C]-1203.55908148148[/C][/ROW]
[ROW][C]21[/C][C]4107.23[/C][C]5028.83241481481[/C][C]-921.602414814815[/C][/ROW]
[ROW][C]22[/C][C]4101.71[/C][C]5030.42019259259[/C][C]-928.710192592593[/C][/ROW]
[ROW][C]23[/C][C]4162.34[/C][C]5066.2613037037[/C][C]-903.921303703704[/C][/ROW]
[ROW][C]24[/C][C]4136.22[/C][C]5025.8413037037[/C][C]-889.621303703704[/C][/ROW]
[ROW][C]25[/C][C]4125.88[/C][C]5405.82954074074[/C][C]-1279.94954074074[/C][/ROW]
[ROW][C]26[/C][C]4031.48[/C][C]5471.46398518519[/C][C]-1439.98398518519[/C][/ROW]
[ROW][C]27[/C][C]3761.36[/C][C]5432.20954074074[/C][C]-1670.84954074074[/C][/ROW]
[ROW][C]28[/C][C]3408.56[/C][C]5172.78574814815[/C][C]-1764.22574814815[/C][/ROW]
[ROW][C]29[/C][C]3228.47[/C][C]5169.1613037037[/C][C]-1940.69130370370[/C][/ROW]
[ROW][C]30[/C][C]3090.45[/C][C]5104.06685925926[/C][C]-2013.61685925926[/C][/ROW]
[ROW][C]31[/C][C]2741.14[/C][C]4930.48685925926[/C][C]-2189.34685925926[/C][/ROW]
[ROW][C]32[/C][C]2980.44[/C][C]4924.01908148148[/C][C]-1943.57908148148[/C][/ROW]
[ROW][C]33[/C][C]3104.33[/C][C]5028.83241481481[/C][C]-1924.50241481482[/C][/ROW]
[ROW][C]34[/C][C]3181.57[/C][C]5030.42019259259[/C][C]-1848.85019259259[/C][/ROW]
[ROW][C]35[/C][C]2863.86[/C][C]5066.2613037037[/C][C]-2202.40130370370[/C][/ROW]
[ROW][C]36[/C][C]2898.01[/C][C]5025.8413037037[/C][C]-2127.83130370370[/C][/ROW]
[ROW][C]37[/C][C]3112.33[/C][C]5405.82954074074[/C][C]-2293.49954074074[/C][/ROW]
[ROW][C]38[/C][C]3254.33[/C][C]5471.46398518518[/C][C]-2217.13398518518[/C][/ROW]
[ROW][C]39[/C][C]3513.47[/C][C]5432.20954074074[/C][C]-1918.73954074074[/C][/ROW]
[ROW][C]40[/C][C]3587.61[/C][C]5172.78574814815[/C][C]-1585.17574814815[/C][/ROW]
[ROW][C]41[/C][C]3727.45[/C][C]5169.1613037037[/C][C]-1441.71130370370[/C][/ROW]
[ROW][C]42[/C][C]3793.34[/C][C]5104.06685925926[/C][C]-1310.72685925926[/C][/ROW]
[ROW][C]43[/C][C]3817.58[/C][C]4930.48685925926[/C][C]-1112.90685925926[/C][/ROW]
[ROW][C]44[/C][C]3845.13[/C][C]4924.01908148148[/C][C]-1078.88908148148[/C][/ROW]
[ROW][C]45[/C][C]3931.86[/C][C]5028.83241481481[/C][C]-1096.97241481482[/C][/ROW]
[ROW][C]46[/C][C]4197.52[/C][C]5030.42019259259[/C][C]-832.900192592592[/C][/ROW]
[ROW][C]47[/C][C]4307.13[/C][C]5066.2613037037[/C][C]-759.131303703704[/C][/ROW]
[ROW][C]48[/C][C]4229.43[/C][C]5025.8413037037[/C][C]-796.411303703704[/C][/ROW]
[ROW][C]49[/C][C]4362.28[/C][C]5405.82954074074[/C][C]-1043.54954074074[/C][/ROW]
[ROW][C]50[/C][C]4217.34[/C][C]5471.46398518519[/C][C]-1254.12398518519[/C][/ROW]
[ROW][C]51[/C][C]4361.28[/C][C]5432.20954074074[/C][C]-1070.92954074074[/C][/ROW]
[ROW][C]52[/C][C]4327.74[/C][C]5172.78574814815[/C][C]-845.045748148149[/C][/ROW]
[ROW][C]53[/C][C]4417.65[/C][C]5169.1613037037[/C][C]-751.511303703704[/C][/ROW]
[ROW][C]54[/C][C]4557.68[/C][C]5104.06685925926[/C][C]-546.386859259259[/C][/ROW]
[ROW][C]55[/C][C]4650.35[/C][C]4930.48685925926[/C][C]-280.136859259259[/C][/ROW]
[ROW][C]56[/C][C]4967.18[/C][C]4924.01908148148[/C][C]43.1609185185189[/C][/ROW]
[ROW][C]57[/C][C]5123.42[/C][C]5028.83241481481[/C][C]94.5875851851853[/C][/ROW]
[ROW][C]58[/C][C]5290.85[/C][C]5030.42019259259[/C][C]260.429807407408[/C][/ROW]
[ROW][C]59[/C][C]5535.66[/C][C]5066.2613037037[/C][C]469.398696296296[/C][/ROW]
[ROW][C]60[/C][C]5514.06[/C][C]5025.8413037037[/C][C]488.218696296296[/C][/ROW]
[ROW][C]61[/C][C]5493.88[/C][C]5405.82954074074[/C][C]88.0504592592574[/C][/ROW]
[ROW][C]62[/C][C]5694.83[/C][C]5471.46398518519[/C][C]223.366014814815[/C][/ROW]
[ROW][C]63[/C][C]5850.41[/C][C]5432.20954074074[/C][C]418.200459259259[/C][/ROW]
[ROW][C]64[/C][C]6116.64[/C][C]5172.78574814815[/C][C]943.854251851852[/C][/ROW]
[ROW][C]65[/C][C]6175[/C][C]5169.1613037037[/C][C]1005.83869629630[/C][/ROW]
[ROW][C]66[/C][C]6513.58[/C][C]5104.06685925926[/C][C]1409.51314074074[/C][/ROW]
[ROW][C]67[/C][C]6383.78[/C][C]4930.48685925926[/C][C]1453.29314074074[/C][/ROW]
[ROW][C]68[/C][C]6673.66[/C][C]4924.01908148148[/C][C]1749.64091851852[/C][/ROW]
[ROW][C]69[/C][C]6936.61[/C][C]5028.83241481481[/C][C]1907.77758518518[/C][/ROW]
[ROW][C]70[/C][C]7300.68[/C][C]5030.42019259259[/C][C]2270.25980740741[/C][/ROW]
[ROW][C]71[/C][C]7392.93[/C][C]5066.2613037037[/C][C]2326.66869629630[/C][/ROW]
[ROW][C]72[/C][C]7497.31[/C][C]5025.8413037037[/C][C]2471.46869629630[/C][/ROW]
[ROW][C]73[/C][C]7584.71[/C][C]5405.82954074074[/C][C]2178.88045925926[/C][/ROW]
[ROW][C]74[/C][C]7160.79[/C][C]5471.46398518519[/C][C]1689.32601481481[/C][/ROW]
[ROW][C]75[/C][C]7196.19[/C][C]5432.20954074074[/C][C]1763.98045925926[/C][/ROW]
[ROW][C]76[/C][C]7245.63[/C][C]5172.78574814815[/C][C]2072.84425185185[/C][/ROW]
[ROW][C]77[/C][C]7347.51[/C][C]5169.1613037037[/C][C]2178.34869629630[/C][/ROW]
[ROW][C]78[/C][C]7425.75[/C][C]5104.06685925926[/C][C]2321.68314074074[/C][/ROW]
[ROW][C]79[/C][C]7778.51[/C][C]4930.48685925926[/C][C]2848.02314074074[/C][/ROW]
[ROW][C]80[/C][C]7822.33[/C][C]4924.01908148148[/C][C]2898.31091851852[/C][/ROW]
[ROW][C]81[/C][C]8181.22[/C][C]5028.83241481481[/C][C]3152.38758518519[/C][/ROW]
[ROW][C]82[/C][C]8371.47[/C][C]5030.42019259259[/C][C]3341.04980740741[/C][/ROW]
[ROW][C]83[/C][C]8347.71[/C][C]5066.2613037037[/C][C]3281.44869629630[/C][/ROW]
[ROW][C]84[/C][C]8672.11[/C][C]5025.8413037037[/C][C]3646.26869629630[/C][/ROW]
[ROW][C]85[/C][C]8802.79[/C][C]5405.82954074074[/C][C]3396.96045925926[/C][/ROW]
[ROW][C]86[/C][C]9138.46[/C][C]5471.46398518518[/C][C]3666.99601481481[/C][/ROW]
[ROW][C]87[/C][C]9123.29[/C][C]5432.20954074074[/C][C]3691.08045925926[/C][/ROW]
[ROW][C]88[/C][C]9023.21[/C][C]7090.45988148148[/C][C]1932.75011851852[/C][/ROW]
[ROW][C]89[/C][C]8850.41[/C][C]7086.83543703704[/C][C]1763.57456296296[/C][/ROW]
[ROW][C]90[/C][C]8864.58[/C][C]7021.74099259259[/C][C]1842.83900740741[/C][/ROW]
[ROW][C]91[/C][C]9163.74[/C][C]6848.1609925926[/C][C]2315.57900740741[/C][/ROW]
[ROW][C]92[/C][C]8516.66[/C][C]6841.69321481481[/C][C]1674.96678518519[/C][/ROW]
[ROW][C]93[/C][C]8553.44[/C][C]6946.50654814815[/C][C]1606.93345185185[/C][/ROW]
[ROW][C]94[/C][C]7555.2[/C][C]6948.09432592593[/C][C]607.105674074074[/C][/ROW]
[ROW][C]95[/C][C]7851.22[/C][C]6983.93543703704[/C][C]867.284562962964[/C][/ROW]
[ROW][C]96[/C][C]7442[/C][C]6943.51543703704[/C][C]498.484562962963[/C][/ROW]
[ROW][C]97[/C][C]7992.53[/C][C]7323.50367407408[/C][C]669.026325925924[/C][/ROW]
[ROW][C]98[/C][C]8264.04[/C][C]7389.13811851852[/C][C]874.901881481483[/C][/ROW]
[ROW][C]99[/C][C]7517.39[/C][C]7349.88367407407[/C][C]167.506325925927[/C][/ROW]
[ROW][C]100[/C][C]7200.4[/C][C]7090.45988148148[/C][C]109.940118518518[/C][/ROW]
[ROW][C]101[/C][C]7193.69[/C][C]7086.83543703704[/C][C]106.854562962963[/C][/ROW]
[ROW][C]102[/C][C]6193.58[/C][C]7021.74099259259[/C][C]-828.160992592592[/C][/ROW]
[ROW][C]103[/C][C]5104.21[/C][C]6848.16099259259[/C][C]-1743.95099259259[/C][/ROW]
[ROW][C]104[/C][C]4800.46[/C][C]6841.69321481482[/C][C]-2041.23321481482[/C][/ROW]
[ROW][C]105[/C][C]4461.61[/C][C]6946.50654814815[/C][C]-2484.89654814815[/C][/ROW]
[ROW][C]106[/C][C]4398.59[/C][C]6948.09432592593[/C][C]-2549.50432592593[/C][/ROW]
[ROW][C]107[/C][C]4243.63[/C][C]6983.93543703704[/C][C]-2740.30543703704[/C][/ROW]
[ROW][C]108[/C][C]4293.82[/C][C]6943.51543703704[/C][C]-2649.69543703704[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57807&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57807&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
14716.995405.82954074073-688.839540740731
24926.655471.46398518519-544.813985185187
34920.15432.20954074074-512.109540740741
45170.095172.78574814815-2.69574814814985
55246.245169.161303703777.0786962962968
65283.615104.06685925926179.54314074074
74979.054930.4868592592648.5631407407415
84825.24924.01908148148-98.819081481482
94695.125028.83241481481-333.712414814815
104711.545030.42019259259-318.880192592593
114727.225066.2613037037-339.041303703704
124384.965025.8413037037-640.881303703704
134378.755405.82954074074-1027.07954074074
144472.935471.46398518519-998.533985185185
154564.075432.20954074074-868.139540740741
164310.545172.78574814815-862.245748148148
174171.385169.1613037037-997.781303703704
184049.385104.06685925926-1054.68685925926
193591.374930.48685925926-1339.11685925926
203720.464924.01908148148-1203.55908148148
214107.235028.83241481481-921.602414814815
224101.715030.42019259259-928.710192592593
234162.345066.2613037037-903.921303703704
244136.225025.8413037037-889.621303703704
254125.885405.82954074074-1279.94954074074
264031.485471.46398518519-1439.98398518519
273761.365432.20954074074-1670.84954074074
283408.565172.78574814815-1764.22574814815
293228.475169.1613037037-1940.69130370370
303090.455104.06685925926-2013.61685925926
312741.144930.48685925926-2189.34685925926
322980.444924.01908148148-1943.57908148148
333104.335028.83241481481-1924.50241481482
343181.575030.42019259259-1848.85019259259
352863.865066.2613037037-2202.40130370370
362898.015025.8413037037-2127.83130370370
373112.335405.82954074074-2293.49954074074
383254.335471.46398518518-2217.13398518518
393513.475432.20954074074-1918.73954074074
403587.615172.78574814815-1585.17574814815
413727.455169.1613037037-1441.71130370370
423793.345104.06685925926-1310.72685925926
433817.584930.48685925926-1112.90685925926
443845.134924.01908148148-1078.88908148148
453931.865028.83241481481-1096.97241481482
464197.525030.42019259259-832.900192592592
474307.135066.2613037037-759.131303703704
484229.435025.8413037037-796.411303703704
494362.285405.82954074074-1043.54954074074
504217.345471.46398518519-1254.12398518519
514361.285432.20954074074-1070.92954074074
524327.745172.78574814815-845.045748148149
534417.655169.1613037037-751.511303703704
544557.685104.06685925926-546.386859259259
554650.354930.48685925926-280.136859259259
564967.184924.0190814814843.1609185185189
575123.425028.8324148148194.5875851851853
585290.855030.42019259259260.429807407408
595535.665066.2613037037469.398696296296
605514.065025.8413037037488.218696296296
615493.885405.8295407407488.0504592592574
625694.835471.46398518519223.366014814815
635850.415432.20954074074418.200459259259
646116.645172.78574814815943.854251851852
6561755169.16130370371005.83869629630
666513.585104.066859259261409.51314074074
676383.784930.486859259261453.29314074074
686673.664924.019081481481749.64091851852
696936.615028.832414814811907.77758518518
707300.685030.420192592592270.25980740741
717392.935066.26130370372326.66869629630
727497.315025.84130370372471.46869629630
737584.715405.829540740742178.88045925926
747160.795471.463985185191689.32601481481
757196.195432.209540740741763.98045925926
767245.635172.785748148152072.84425185185
777347.515169.16130370372178.34869629630
787425.755104.066859259262321.68314074074
797778.514930.486859259262848.02314074074
807822.334924.019081481482898.31091851852
818181.225028.832414814813152.38758518519
828371.475030.420192592593341.04980740741
838347.715066.26130370373281.44869629630
848672.115025.84130370373646.26869629630
858802.795405.829540740743396.96045925926
869138.465471.463985185183666.99601481481
879123.295432.209540740743691.08045925926
889023.217090.459881481481932.75011851852
898850.417086.835437037041763.57456296296
908864.587021.740992592591842.83900740741
919163.746848.16099259262315.57900740741
928516.666841.693214814811674.96678518519
938553.446946.506548148151606.93345185185
947555.26948.09432592593607.105674074074
957851.226983.93543703704867.284562962964
9674426943.51543703704498.484562962963
977992.537323.50367407408669.026325925924
988264.047389.13811851852874.901881481483
997517.397349.88367407407167.506325925927
1007200.47090.45988148148109.940118518518
1017193.697086.83543703704106.854562962963
1026193.587021.74099259259-828.160992592592
1035104.216848.16099259259-1743.95099259259
1044800.466841.69321481482-2041.23321481482
1054461.616946.50654814815-2484.89654814815
1064398.596948.09432592593-2549.50432592593
1074243.636983.93543703704-2740.30543703704
1084293.826943.51543703704-2649.69543703704







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.01614937226336330.03229874452672660.983850627736637
170.01109203846498390.02218407692996790.988907961535016
180.0084817771757210.0169635543514420.99151822282428
190.007215770277853820.01443154055570760.992784229722146
200.004180420699474070.008360841398948150.995819579300526
210.001613715572870590.003227431145741170.99838628442713
220.0006198449098054750.001239689819610950.999380155090195
230.0002276936411265460.0004553872822530910.999772306358873
247.0470865500154e-050.0001409417310003080.9999295291345
252.4408409357394e-054.8816818714788e-050.999975591590643
261.07820536502237e-052.15641073004474e-050.99998921794635
277.6227350740456e-061.52454701480912e-050.999992377264926
289.81585728745823e-061.96317145749165e-050.999990184142713
291.50606777528912e-053.01213555057824e-050.999984939322247
302.41058766683406e-054.82117533366811e-050.999975894123332
313.44473682396855e-056.8894736479371e-050.99996555263176
323.31299281407547e-056.62598562815094e-050.99996687007186
333.19148282496544e-056.38296564993089e-050.99996808517175
342.83634919176705e-055.6726983835341e-050.999971636508082
353.9720857615039e-057.9441715230078e-050.999960279142385
364.36955986462112e-058.73911972924225e-050.999956304401354
375.50488743329777e-050.0001100977486659550.999944951125667
386.54611111535887e-050.0001309222223071770.999934538888846
395.82137465209906e-050.0001164274930419810.99994178625348
404.45116965284349e-058.90233930568698e-050.999955488303472
413.09104836153634e-056.18209672307267e-050.999969089516385
422.02704486598603e-054.05408973197206e-050.99997972955134
431.25639997969115e-052.51279995938229e-050.999987436000203
447.72809474217294e-061.54561894843459e-050.999992271905258
454.8535222441559e-069.7070444883118e-060.999995146477756
462.86431080529572e-065.72862161059143e-060.999997135689195
471.78696062065140e-063.57392124130280e-060.99999821303938
481.17937369108643e-062.35874738217286e-060.999998820626309
499.94140967919774e-071.98828193583955e-060.999999005859032
509.63824738967007e-071.92764947793401e-060.999999036175261
518.80005508906628e-071.76001101781326e-060.999999119994491
528.32872924557219e-071.66574584911444e-060.999999167127075
538.41734762364736e-071.68346952472947e-060.999999158265238
548.52544967059152e-071.70508993411830e-060.999999147455033
551.05251835850552e-062.10503671701103e-060.999998947481642
561.38280879262959e-062.76561758525919e-060.999998617191207
571.86254652030471e-063.72509304060941e-060.99999813745348
582.42476349161769e-064.84952698323537e-060.999997575236508
594.00738762400137e-068.01477524800275e-060.999995992612376
607.10801503442135e-061.42160300688427e-050.999992891984966
611.69756636969691e-053.39513273939382e-050.999983024336303
624.40023181114045e-058.8004636222809e-050.999955997681889
639.82397281226553e-050.0001964794562453110.999901760271877
640.0002556371224794710.0005112742449589410.99974436287752
650.0006252391102542360.001250478220508470.999374760889746
660.001439048486110540.002878096972221090.99856095151389
670.00303696964565040.00607393929130080.99696303035435
680.005490076095249770.01098015219049950.99450992390475
690.009174658256150450.01834931651230090.99082534174385
700.01500953755595440.03001907511190880.984990462444046
710.02236627945770950.04473255891541890.97763372054229
720.03256660229788640.06513320459577280.967433397702114
730.04755542461632890.09511084923265780.95244457538367
740.06487974443764850.1297594888752970.935120255562351
750.07702407698783680.1540481539756740.922975923012163
760.09771958856949240.1954391771389850.902280411430508
770.1220944038609440.2441888077218880.877905596139056
780.1366603547691680.2733207095383360.863339645230832
790.1486574615032450.2973149230064900.851342538496755
800.1475171967324330.2950343934648650.852482803267567
810.1435554135431290.2871108270862580.856444586456871
820.1379602211702860.2759204423405710.862039778829714
830.1275341642716950.2550683285433890.872465835728305
840.1316189827397710.2632379654795430.868381017260229
850.1190890109483670.2381780218967340.880910989051633
860.1083177678817340.2166355357634680.891682232118266
870.0916415751211890.1832831502423780.908358424878811
880.06553962302024870.1310792460404970.934460376979751
890.04315376491204580.08630752982409160.956846235087954
900.03365026191790930.06730052383581860.96634973808209
910.04390633415119950.0878126683023990.9560936658488
920.05003815718634450.1000763143726890.949961842813655

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.0161493722633633 & 0.0322987445267266 & 0.983850627736637 \tabularnewline
17 & 0.0110920384649839 & 0.0221840769299679 & 0.988907961535016 \tabularnewline
18 & 0.008481777175721 & 0.016963554351442 & 0.99151822282428 \tabularnewline
19 & 0.00721577027785382 & 0.0144315405557076 & 0.992784229722146 \tabularnewline
20 & 0.00418042069947407 & 0.00836084139894815 & 0.995819579300526 \tabularnewline
21 & 0.00161371557287059 & 0.00322743114574117 & 0.99838628442713 \tabularnewline
22 & 0.000619844909805475 & 0.00123968981961095 & 0.999380155090195 \tabularnewline
23 & 0.000227693641126546 & 0.000455387282253091 & 0.999772306358873 \tabularnewline
24 & 7.0470865500154e-05 & 0.000140941731000308 & 0.9999295291345 \tabularnewline
25 & 2.4408409357394e-05 & 4.8816818714788e-05 & 0.999975591590643 \tabularnewline
26 & 1.07820536502237e-05 & 2.15641073004474e-05 & 0.99998921794635 \tabularnewline
27 & 7.6227350740456e-06 & 1.52454701480912e-05 & 0.999992377264926 \tabularnewline
28 & 9.81585728745823e-06 & 1.96317145749165e-05 & 0.999990184142713 \tabularnewline
29 & 1.50606777528912e-05 & 3.01213555057824e-05 & 0.999984939322247 \tabularnewline
30 & 2.41058766683406e-05 & 4.82117533366811e-05 & 0.999975894123332 \tabularnewline
31 & 3.44473682396855e-05 & 6.8894736479371e-05 & 0.99996555263176 \tabularnewline
32 & 3.31299281407547e-05 & 6.62598562815094e-05 & 0.99996687007186 \tabularnewline
33 & 3.19148282496544e-05 & 6.38296564993089e-05 & 0.99996808517175 \tabularnewline
34 & 2.83634919176705e-05 & 5.6726983835341e-05 & 0.999971636508082 \tabularnewline
35 & 3.9720857615039e-05 & 7.9441715230078e-05 & 0.999960279142385 \tabularnewline
36 & 4.36955986462112e-05 & 8.73911972924225e-05 & 0.999956304401354 \tabularnewline
37 & 5.50488743329777e-05 & 0.000110097748665955 & 0.999944951125667 \tabularnewline
38 & 6.54611111535887e-05 & 0.000130922222307177 & 0.999934538888846 \tabularnewline
39 & 5.82137465209906e-05 & 0.000116427493041981 & 0.99994178625348 \tabularnewline
40 & 4.45116965284349e-05 & 8.90233930568698e-05 & 0.999955488303472 \tabularnewline
41 & 3.09104836153634e-05 & 6.18209672307267e-05 & 0.999969089516385 \tabularnewline
42 & 2.02704486598603e-05 & 4.05408973197206e-05 & 0.99997972955134 \tabularnewline
43 & 1.25639997969115e-05 & 2.51279995938229e-05 & 0.999987436000203 \tabularnewline
44 & 7.72809474217294e-06 & 1.54561894843459e-05 & 0.999992271905258 \tabularnewline
45 & 4.8535222441559e-06 & 9.7070444883118e-06 & 0.999995146477756 \tabularnewline
46 & 2.86431080529572e-06 & 5.72862161059143e-06 & 0.999997135689195 \tabularnewline
47 & 1.78696062065140e-06 & 3.57392124130280e-06 & 0.99999821303938 \tabularnewline
48 & 1.17937369108643e-06 & 2.35874738217286e-06 & 0.999998820626309 \tabularnewline
49 & 9.94140967919774e-07 & 1.98828193583955e-06 & 0.999999005859032 \tabularnewline
50 & 9.63824738967007e-07 & 1.92764947793401e-06 & 0.999999036175261 \tabularnewline
51 & 8.80005508906628e-07 & 1.76001101781326e-06 & 0.999999119994491 \tabularnewline
52 & 8.32872924557219e-07 & 1.66574584911444e-06 & 0.999999167127075 \tabularnewline
53 & 8.41734762364736e-07 & 1.68346952472947e-06 & 0.999999158265238 \tabularnewline
54 & 8.52544967059152e-07 & 1.70508993411830e-06 & 0.999999147455033 \tabularnewline
55 & 1.05251835850552e-06 & 2.10503671701103e-06 & 0.999998947481642 \tabularnewline
56 & 1.38280879262959e-06 & 2.76561758525919e-06 & 0.999998617191207 \tabularnewline
57 & 1.86254652030471e-06 & 3.72509304060941e-06 & 0.99999813745348 \tabularnewline
58 & 2.42476349161769e-06 & 4.84952698323537e-06 & 0.999997575236508 \tabularnewline
59 & 4.00738762400137e-06 & 8.01477524800275e-06 & 0.999995992612376 \tabularnewline
60 & 7.10801503442135e-06 & 1.42160300688427e-05 & 0.999992891984966 \tabularnewline
61 & 1.69756636969691e-05 & 3.39513273939382e-05 & 0.999983024336303 \tabularnewline
62 & 4.40023181114045e-05 & 8.8004636222809e-05 & 0.999955997681889 \tabularnewline
63 & 9.82397281226553e-05 & 0.000196479456245311 & 0.999901760271877 \tabularnewline
64 & 0.000255637122479471 & 0.000511274244958941 & 0.99974436287752 \tabularnewline
65 & 0.000625239110254236 & 0.00125047822050847 & 0.999374760889746 \tabularnewline
66 & 0.00143904848611054 & 0.00287809697222109 & 0.99856095151389 \tabularnewline
67 & 0.0030369696456504 & 0.0060739392913008 & 0.99696303035435 \tabularnewline
68 & 0.00549007609524977 & 0.0109801521904995 & 0.99450992390475 \tabularnewline
69 & 0.00917465825615045 & 0.0183493165123009 & 0.99082534174385 \tabularnewline
70 & 0.0150095375559544 & 0.0300190751119088 & 0.984990462444046 \tabularnewline
71 & 0.0223662794577095 & 0.0447325589154189 & 0.97763372054229 \tabularnewline
72 & 0.0325666022978864 & 0.0651332045957728 & 0.967433397702114 \tabularnewline
73 & 0.0475554246163289 & 0.0951108492326578 & 0.95244457538367 \tabularnewline
74 & 0.0648797444376485 & 0.129759488875297 & 0.935120255562351 \tabularnewline
75 & 0.0770240769878368 & 0.154048153975674 & 0.922975923012163 \tabularnewline
76 & 0.0977195885694924 & 0.195439177138985 & 0.902280411430508 \tabularnewline
77 & 0.122094403860944 & 0.244188807721888 & 0.877905596139056 \tabularnewline
78 & 0.136660354769168 & 0.273320709538336 & 0.863339645230832 \tabularnewline
79 & 0.148657461503245 & 0.297314923006490 & 0.851342538496755 \tabularnewline
80 & 0.147517196732433 & 0.295034393464865 & 0.852482803267567 \tabularnewline
81 & 0.143555413543129 & 0.287110827086258 & 0.856444586456871 \tabularnewline
82 & 0.137960221170286 & 0.275920442340571 & 0.862039778829714 \tabularnewline
83 & 0.127534164271695 & 0.255068328543389 & 0.872465835728305 \tabularnewline
84 & 0.131618982739771 & 0.263237965479543 & 0.868381017260229 \tabularnewline
85 & 0.119089010948367 & 0.238178021896734 & 0.880910989051633 \tabularnewline
86 & 0.108317767881734 & 0.216635535763468 & 0.891682232118266 \tabularnewline
87 & 0.091641575121189 & 0.183283150242378 & 0.908358424878811 \tabularnewline
88 & 0.0655396230202487 & 0.131079246040497 & 0.934460376979751 \tabularnewline
89 & 0.0431537649120458 & 0.0863075298240916 & 0.956846235087954 \tabularnewline
90 & 0.0336502619179093 & 0.0673005238358186 & 0.96634973808209 \tabularnewline
91 & 0.0439063341511995 & 0.087812668302399 & 0.9560936658488 \tabularnewline
92 & 0.0500381571863445 & 0.100076314372689 & 0.949961842813655 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57807&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]16[/C][C]0.0161493722633633[/C][C]0.0322987445267266[/C][C]0.983850627736637[/C][/ROW]
[ROW][C]17[/C][C]0.0110920384649839[/C][C]0.0221840769299679[/C][C]0.988907961535016[/C][/ROW]
[ROW][C]18[/C][C]0.008481777175721[/C][C]0.016963554351442[/C][C]0.99151822282428[/C][/ROW]
[ROW][C]19[/C][C]0.00721577027785382[/C][C]0.0144315405557076[/C][C]0.992784229722146[/C][/ROW]
[ROW][C]20[/C][C]0.00418042069947407[/C][C]0.00836084139894815[/C][C]0.995819579300526[/C][/ROW]
[ROW][C]21[/C][C]0.00161371557287059[/C][C]0.00322743114574117[/C][C]0.99838628442713[/C][/ROW]
[ROW][C]22[/C][C]0.000619844909805475[/C][C]0.00123968981961095[/C][C]0.999380155090195[/C][/ROW]
[ROW][C]23[/C][C]0.000227693641126546[/C][C]0.000455387282253091[/C][C]0.999772306358873[/C][/ROW]
[ROW][C]24[/C][C]7.0470865500154e-05[/C][C]0.000140941731000308[/C][C]0.9999295291345[/C][/ROW]
[ROW][C]25[/C][C]2.4408409357394e-05[/C][C]4.8816818714788e-05[/C][C]0.999975591590643[/C][/ROW]
[ROW][C]26[/C][C]1.07820536502237e-05[/C][C]2.15641073004474e-05[/C][C]0.99998921794635[/C][/ROW]
[ROW][C]27[/C][C]7.6227350740456e-06[/C][C]1.52454701480912e-05[/C][C]0.999992377264926[/C][/ROW]
[ROW][C]28[/C][C]9.81585728745823e-06[/C][C]1.96317145749165e-05[/C][C]0.999990184142713[/C][/ROW]
[ROW][C]29[/C][C]1.50606777528912e-05[/C][C]3.01213555057824e-05[/C][C]0.999984939322247[/C][/ROW]
[ROW][C]30[/C][C]2.41058766683406e-05[/C][C]4.82117533366811e-05[/C][C]0.999975894123332[/C][/ROW]
[ROW][C]31[/C][C]3.44473682396855e-05[/C][C]6.8894736479371e-05[/C][C]0.99996555263176[/C][/ROW]
[ROW][C]32[/C][C]3.31299281407547e-05[/C][C]6.62598562815094e-05[/C][C]0.99996687007186[/C][/ROW]
[ROW][C]33[/C][C]3.19148282496544e-05[/C][C]6.38296564993089e-05[/C][C]0.99996808517175[/C][/ROW]
[ROW][C]34[/C][C]2.83634919176705e-05[/C][C]5.6726983835341e-05[/C][C]0.999971636508082[/C][/ROW]
[ROW][C]35[/C][C]3.9720857615039e-05[/C][C]7.9441715230078e-05[/C][C]0.999960279142385[/C][/ROW]
[ROW][C]36[/C][C]4.36955986462112e-05[/C][C]8.73911972924225e-05[/C][C]0.999956304401354[/C][/ROW]
[ROW][C]37[/C][C]5.50488743329777e-05[/C][C]0.000110097748665955[/C][C]0.999944951125667[/C][/ROW]
[ROW][C]38[/C][C]6.54611111535887e-05[/C][C]0.000130922222307177[/C][C]0.999934538888846[/C][/ROW]
[ROW][C]39[/C][C]5.82137465209906e-05[/C][C]0.000116427493041981[/C][C]0.99994178625348[/C][/ROW]
[ROW][C]40[/C][C]4.45116965284349e-05[/C][C]8.90233930568698e-05[/C][C]0.999955488303472[/C][/ROW]
[ROW][C]41[/C][C]3.09104836153634e-05[/C][C]6.18209672307267e-05[/C][C]0.999969089516385[/C][/ROW]
[ROW][C]42[/C][C]2.02704486598603e-05[/C][C]4.05408973197206e-05[/C][C]0.99997972955134[/C][/ROW]
[ROW][C]43[/C][C]1.25639997969115e-05[/C][C]2.51279995938229e-05[/C][C]0.999987436000203[/C][/ROW]
[ROW][C]44[/C][C]7.72809474217294e-06[/C][C]1.54561894843459e-05[/C][C]0.999992271905258[/C][/ROW]
[ROW][C]45[/C][C]4.8535222441559e-06[/C][C]9.7070444883118e-06[/C][C]0.999995146477756[/C][/ROW]
[ROW][C]46[/C][C]2.86431080529572e-06[/C][C]5.72862161059143e-06[/C][C]0.999997135689195[/C][/ROW]
[ROW][C]47[/C][C]1.78696062065140e-06[/C][C]3.57392124130280e-06[/C][C]0.99999821303938[/C][/ROW]
[ROW][C]48[/C][C]1.17937369108643e-06[/C][C]2.35874738217286e-06[/C][C]0.999998820626309[/C][/ROW]
[ROW][C]49[/C][C]9.94140967919774e-07[/C][C]1.98828193583955e-06[/C][C]0.999999005859032[/C][/ROW]
[ROW][C]50[/C][C]9.63824738967007e-07[/C][C]1.92764947793401e-06[/C][C]0.999999036175261[/C][/ROW]
[ROW][C]51[/C][C]8.80005508906628e-07[/C][C]1.76001101781326e-06[/C][C]0.999999119994491[/C][/ROW]
[ROW][C]52[/C][C]8.32872924557219e-07[/C][C]1.66574584911444e-06[/C][C]0.999999167127075[/C][/ROW]
[ROW][C]53[/C][C]8.41734762364736e-07[/C][C]1.68346952472947e-06[/C][C]0.999999158265238[/C][/ROW]
[ROW][C]54[/C][C]8.52544967059152e-07[/C][C]1.70508993411830e-06[/C][C]0.999999147455033[/C][/ROW]
[ROW][C]55[/C][C]1.05251835850552e-06[/C][C]2.10503671701103e-06[/C][C]0.999998947481642[/C][/ROW]
[ROW][C]56[/C][C]1.38280879262959e-06[/C][C]2.76561758525919e-06[/C][C]0.999998617191207[/C][/ROW]
[ROW][C]57[/C][C]1.86254652030471e-06[/C][C]3.72509304060941e-06[/C][C]0.99999813745348[/C][/ROW]
[ROW][C]58[/C][C]2.42476349161769e-06[/C][C]4.84952698323537e-06[/C][C]0.999997575236508[/C][/ROW]
[ROW][C]59[/C][C]4.00738762400137e-06[/C][C]8.01477524800275e-06[/C][C]0.999995992612376[/C][/ROW]
[ROW][C]60[/C][C]7.10801503442135e-06[/C][C]1.42160300688427e-05[/C][C]0.999992891984966[/C][/ROW]
[ROW][C]61[/C][C]1.69756636969691e-05[/C][C]3.39513273939382e-05[/C][C]0.999983024336303[/C][/ROW]
[ROW][C]62[/C][C]4.40023181114045e-05[/C][C]8.8004636222809e-05[/C][C]0.999955997681889[/C][/ROW]
[ROW][C]63[/C][C]9.82397281226553e-05[/C][C]0.000196479456245311[/C][C]0.999901760271877[/C][/ROW]
[ROW][C]64[/C][C]0.000255637122479471[/C][C]0.000511274244958941[/C][C]0.99974436287752[/C][/ROW]
[ROW][C]65[/C][C]0.000625239110254236[/C][C]0.00125047822050847[/C][C]0.999374760889746[/C][/ROW]
[ROW][C]66[/C][C]0.00143904848611054[/C][C]0.00287809697222109[/C][C]0.99856095151389[/C][/ROW]
[ROW][C]67[/C][C]0.0030369696456504[/C][C]0.0060739392913008[/C][C]0.99696303035435[/C][/ROW]
[ROW][C]68[/C][C]0.00549007609524977[/C][C]0.0109801521904995[/C][C]0.99450992390475[/C][/ROW]
[ROW][C]69[/C][C]0.00917465825615045[/C][C]0.0183493165123009[/C][C]0.99082534174385[/C][/ROW]
[ROW][C]70[/C][C]0.0150095375559544[/C][C]0.0300190751119088[/C][C]0.984990462444046[/C][/ROW]
[ROW][C]71[/C][C]0.0223662794577095[/C][C]0.0447325589154189[/C][C]0.97763372054229[/C][/ROW]
[ROW][C]72[/C][C]0.0325666022978864[/C][C]0.0651332045957728[/C][C]0.967433397702114[/C][/ROW]
[ROW][C]73[/C][C]0.0475554246163289[/C][C]0.0951108492326578[/C][C]0.95244457538367[/C][/ROW]
[ROW][C]74[/C][C]0.0648797444376485[/C][C]0.129759488875297[/C][C]0.935120255562351[/C][/ROW]
[ROW][C]75[/C][C]0.0770240769878368[/C][C]0.154048153975674[/C][C]0.922975923012163[/C][/ROW]
[ROW][C]76[/C][C]0.0977195885694924[/C][C]0.195439177138985[/C][C]0.902280411430508[/C][/ROW]
[ROW][C]77[/C][C]0.122094403860944[/C][C]0.244188807721888[/C][C]0.877905596139056[/C][/ROW]
[ROW][C]78[/C][C]0.136660354769168[/C][C]0.273320709538336[/C][C]0.863339645230832[/C][/ROW]
[ROW][C]79[/C][C]0.148657461503245[/C][C]0.297314923006490[/C][C]0.851342538496755[/C][/ROW]
[ROW][C]80[/C][C]0.147517196732433[/C][C]0.295034393464865[/C][C]0.852482803267567[/C][/ROW]
[ROW][C]81[/C][C]0.143555413543129[/C][C]0.287110827086258[/C][C]0.856444586456871[/C][/ROW]
[ROW][C]82[/C][C]0.137960221170286[/C][C]0.275920442340571[/C][C]0.862039778829714[/C][/ROW]
[ROW][C]83[/C][C]0.127534164271695[/C][C]0.255068328543389[/C][C]0.872465835728305[/C][/ROW]
[ROW][C]84[/C][C]0.131618982739771[/C][C]0.263237965479543[/C][C]0.868381017260229[/C][/ROW]
[ROW][C]85[/C][C]0.119089010948367[/C][C]0.238178021896734[/C][C]0.880910989051633[/C][/ROW]
[ROW][C]86[/C][C]0.108317767881734[/C][C]0.216635535763468[/C][C]0.891682232118266[/C][/ROW]
[ROW][C]87[/C][C]0.091641575121189[/C][C]0.183283150242378[/C][C]0.908358424878811[/C][/ROW]
[ROW][C]88[/C][C]0.0655396230202487[/C][C]0.131079246040497[/C][C]0.934460376979751[/C][/ROW]
[ROW][C]89[/C][C]0.0431537649120458[/C][C]0.0863075298240916[/C][C]0.956846235087954[/C][/ROW]
[ROW][C]90[/C][C]0.0336502619179093[/C][C]0.0673005238358186[/C][C]0.96634973808209[/C][/ROW]
[ROW][C]91[/C][C]0.0439063341511995[/C][C]0.087812668302399[/C][C]0.9560936658488[/C][/ROW]
[ROW][C]92[/C][C]0.0500381571863445[/C][C]0.100076314372689[/C][C]0.949961842813655[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57807&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57807&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
160.01614937226336330.03229874452672660.983850627736637
170.01109203846498390.02218407692996790.988907961535016
180.0084817771757210.0169635543514420.99151822282428
190.007215770277853820.01443154055570760.992784229722146
200.004180420699474070.008360841398948150.995819579300526
210.001613715572870590.003227431145741170.99838628442713
220.0006198449098054750.001239689819610950.999380155090195
230.0002276936411265460.0004553872822530910.999772306358873
247.0470865500154e-050.0001409417310003080.9999295291345
252.4408409357394e-054.8816818714788e-050.999975591590643
261.07820536502237e-052.15641073004474e-050.99998921794635
277.6227350740456e-061.52454701480912e-050.999992377264926
289.81585728745823e-061.96317145749165e-050.999990184142713
291.50606777528912e-053.01213555057824e-050.999984939322247
302.41058766683406e-054.82117533366811e-050.999975894123332
313.44473682396855e-056.8894736479371e-050.99996555263176
323.31299281407547e-056.62598562815094e-050.99996687007186
333.19148282496544e-056.38296564993089e-050.99996808517175
342.83634919176705e-055.6726983835341e-050.999971636508082
353.9720857615039e-057.9441715230078e-050.999960279142385
364.36955986462112e-058.73911972924225e-050.999956304401354
375.50488743329777e-050.0001100977486659550.999944951125667
386.54611111535887e-050.0001309222223071770.999934538888846
395.82137465209906e-050.0001164274930419810.99994178625348
404.45116965284349e-058.90233930568698e-050.999955488303472
413.09104836153634e-056.18209672307267e-050.999969089516385
422.02704486598603e-054.05408973197206e-050.99997972955134
431.25639997969115e-052.51279995938229e-050.999987436000203
447.72809474217294e-061.54561894843459e-050.999992271905258
454.8535222441559e-069.7070444883118e-060.999995146477756
462.86431080529572e-065.72862161059143e-060.999997135689195
471.78696062065140e-063.57392124130280e-060.99999821303938
481.17937369108643e-062.35874738217286e-060.999998820626309
499.94140967919774e-071.98828193583955e-060.999999005859032
509.63824738967007e-071.92764947793401e-060.999999036175261
518.80005508906628e-071.76001101781326e-060.999999119994491
528.32872924557219e-071.66574584911444e-060.999999167127075
538.41734762364736e-071.68346952472947e-060.999999158265238
548.52544967059152e-071.70508993411830e-060.999999147455033
551.05251835850552e-062.10503671701103e-060.999998947481642
561.38280879262959e-062.76561758525919e-060.999998617191207
571.86254652030471e-063.72509304060941e-060.99999813745348
582.42476349161769e-064.84952698323537e-060.999997575236508
594.00738762400137e-068.01477524800275e-060.999995992612376
607.10801503442135e-061.42160300688427e-050.999992891984966
611.69756636969691e-053.39513273939382e-050.999983024336303
624.40023181114045e-058.8004636222809e-050.999955997681889
639.82397281226553e-050.0001964794562453110.999901760271877
640.0002556371224794710.0005112742449589410.99974436287752
650.0006252391102542360.001250478220508470.999374760889746
660.001439048486110540.002878096972221090.99856095151389
670.00303696964565040.00607393929130080.99696303035435
680.005490076095249770.01098015219049950.99450992390475
690.009174658256150450.01834931651230090.99082534174385
700.01500953755595440.03001907511190880.984990462444046
710.02236627945770950.04473255891541890.97763372054229
720.03256660229788640.06513320459577280.967433397702114
730.04755542461632890.09511084923265780.95244457538367
740.06487974443764850.1297594888752970.935120255562351
750.07702407698783680.1540481539756740.922975923012163
760.09771958856949240.1954391771389850.902280411430508
770.1220944038609440.2441888077218880.877905596139056
780.1366603547691680.2733207095383360.863339645230832
790.1486574615032450.2973149230064900.851342538496755
800.1475171967324330.2950343934648650.852482803267567
810.1435554135431290.2871108270862580.856444586456871
820.1379602211702860.2759204423405710.862039778829714
830.1275341642716950.2550683285433890.872465835728305
840.1316189827397710.2632379654795430.868381017260229
850.1190890109483670.2381780218967340.880910989051633
860.1083177678817340.2166355357634680.891682232118266
870.0916415751211890.1832831502423780.908358424878811
880.06553962302024870.1310792460404970.934460376979751
890.04315376491204580.08630752982409160.956846235087954
900.03365026191790930.06730052383581860.96634973808209
910.04390633415119950.0878126683023990.9560936658488
920.05003815718634450.1000763143726890.949961842813655







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level480.623376623376623NOK
5% type I error level560.727272727272727NOK
10% type I error level610.792207792207792NOK

\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 & 48 & 0.623376623376623 & NOK \tabularnewline
5% type I error level & 56 & 0.727272727272727 & NOK \tabularnewline
10% type I error level & 61 & 0.792207792207792 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57807&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]48[/C][C]0.623376623376623[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]56[/C][C]0.727272727272727[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]61[/C][C]0.792207792207792[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57807&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57807&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 level480.623376623376623NOK
5% type I error level560.727272727272727NOK
10% type I error level610.792207792207792NOK



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