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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 computationThu, 19 Nov 2009 08:24:52 -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/t1258644418gc5wjt2luqynn3s.htm/, Retrieved Fri, 29 Mar 2024 09:32:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57761, Retrieved Fri, 29 Mar 2024 09:32:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact141
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [workshop 7 bereke...] [2009-11-19 15:24:52] [78d370e6d5f4594e9982a5085e7604c6] [Current]
-   PD        [Multiple Regression] [DSHW-WS7-Multiple...] [2009-11-20 13:19:23] [f15cfb7053d35072d573abca87df96a0]
-   P           [Multiple Regression] [DSHW-WS7-Multiple...] [2009-11-20 13:53:17] [f15cfb7053d35072d573abca87df96a0]
-    D            [Multiple Regression] [DSHW-WS7-MultRegr1] [2009-11-20 14:59:26] [f15cfb7053d35072d573abca87df96a0]
-    D              [Multiple Regression] [DSHW-WS7-MultRegr...] [2009-11-20 15:10:50] [f15cfb7053d35072d573abca87df96a0]
-   P                 [Multiple Regression] [DSHW-WS7-MiltRegr.2] [2009-11-20 15:42:03] [f15cfb7053d35072d573abca87df96a0]
-    D                  [Multiple Regression] [review 7] [2009-11-24 20:46:35] [309ee52d0058ff0a6f7eec15e07b2d9f]
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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=57761&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=57761&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57761&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] = + 5156.77183908046 + 1866.29530377668X[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  5156.77183908046 +  1866.29530377668X[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57761&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  5156.77183908046 +  1866.29530377668X[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57761&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57761&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] = + 5156.77183908046 + 1866.29530377668X[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5156.77183908046182.02874528.329400
X1866.29530377668412.8023934.5211.6e-058e-06

\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) & 5156.77183908046 & 182.028745 & 28.3294 & 0 & 0 \tabularnewline
X & 1866.29530377668 & 412.802393 & 4.521 & 1.6e-05 & 8e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57761&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]5156.77183908046[/C][C]182.028745[/C][C]28.3294[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]1866.29530377668[/C][C]412.802393[/C][C]4.521[/C][C]1.6e-05[/C][C]8e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57761&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57761&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)5156.77183908046182.02874528.329400
X1866.29530377668412.8023934.5211.6e-058e-06







Multiple Linear Regression - Regression Statistics
Multiple R0.402065013518833
R-squared0.161656275095899
Adjusted R-squared0.153747372030766
F-TEST (value)20.4397846028196
F-TEST (DF numerator)1
F-TEST (DF denominator)106
p-value1.60881629114318e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1697.85110548636
Sum Squared Residuals305566027.898534

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.402065013518833 \tabularnewline
R-squared & 0.161656275095899 \tabularnewline
Adjusted R-squared & 0.153747372030766 \tabularnewline
F-TEST (value) & 20.4397846028196 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 106 \tabularnewline
p-value & 1.60881629114318e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1697.85110548636 \tabularnewline
Sum Squared Residuals & 305566027.898534 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57761&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.402065013518833[/C][/ROW]
[ROW][C]R-squared[/C][C]0.161656275095899[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.153747372030766[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]20.4397846028196[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]106[/C][/ROW]
[ROW][C]p-value[/C][C]1.60881629114318e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1697.85110548636[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]305566027.898534[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57761&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57761&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 R0.402065013518833
R-squared0.161656275095899
Adjusted R-squared0.153747372030766
F-TEST (value)20.4397846028196
F-TEST (DF numerator)1
F-TEST (DF denominator)106
p-value1.60881629114318e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1697.85110548636
Sum Squared Residuals305566027.898534







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14716.995156.77183908045-439.781839080449
24926.655156.77183908046-230.121839080459
34920.15156.77183908046-236.671839080460
45170.095156.7718390804613.3181609195402
55246.245156.7718390804689.4681609195398
65283.615156.77183908046126.838160919540
74979.055156.77183908046-177.72183908046
84825.25156.77183908046-331.57183908046
94695.125156.77183908046-461.65183908046
104711.545156.77183908046-445.23183908046
114727.225156.77183908046-429.551839080460
124384.965156.77183908046-771.81183908046
134378.755156.77183908046-778.02183908046
144472.935156.77183908046-683.84183908046
154564.075156.77183908046-592.70183908046
164310.545156.77183908046-846.23183908046
174171.385156.77183908046-985.39183908046
184049.385156.77183908046-1107.39183908046
193591.375156.77183908046-1565.40183908046
203720.465156.77183908046-1436.31183908046
214107.235156.77183908046-1049.54183908046
224101.715156.77183908046-1055.06183908046
234162.345156.77183908046-994.43183908046
244136.225156.77183908046-1020.55183908046
254125.885156.77183908046-1030.89183908046
264031.485156.77183908046-1125.29183908046
273761.365156.77183908046-1395.41183908046
283408.565156.77183908046-1748.21183908046
293228.475156.77183908046-1928.30183908046
303090.455156.77183908046-2066.32183908046
312741.145156.77183908046-2415.63183908046
322980.445156.77183908046-2176.33183908046
333104.335156.77183908046-2052.44183908046
343181.575156.77183908046-1975.20183908046
352863.865156.77183908046-2292.91183908046
362898.015156.77183908046-2258.76183908046
373112.335156.77183908046-2044.44183908046
383254.335156.77183908046-1902.44183908046
393513.475156.77183908046-1643.30183908046
403587.615156.77183908046-1569.16183908046
413727.455156.77183908046-1429.32183908046
423793.345156.77183908046-1363.43183908046
433817.585156.77183908046-1339.19183908046
443845.135156.77183908046-1311.64183908046
453931.865156.77183908046-1224.91183908046
464197.525156.77183908046-959.25183908046
474307.135156.77183908046-849.64183908046
484229.435156.77183908046-927.34183908046
494362.285156.77183908046-794.49183908046
504217.345156.77183908046-939.43183908046
514361.285156.77183908046-795.49183908046
524327.745156.77183908046-829.03183908046
534417.655156.77183908046-739.12183908046
544557.685156.77183908046-599.09183908046
554650.355156.77183908046-506.42183908046
564967.185156.77183908046-189.591839080460
575123.425156.77183908046-33.3518390804599
585290.855156.77183908046134.078160919540
595535.665156.77183908046378.88816091954
605514.065156.77183908046357.288160919540
615493.885156.77183908046337.10816091954
625694.835156.77183908046538.05816091954
635850.415156.77183908046693.63816091954
646116.645156.77183908046959.86816091954
6561755156.771839080461018.22816091954
666513.585156.771839080461356.80816091954
676383.785156.771839080461227.00816091954
686673.665156.771839080461516.88816091954
696936.615156.771839080461779.83816091954
707300.685156.771839080462143.90816091954
717392.935156.771839080462236.15816091954
727497.315156.771839080462340.53816091954
737584.715156.771839080462427.93816091954
747160.795156.771839080462004.01816091954
757196.195156.771839080462039.41816091954
767245.635156.771839080462088.85816091954
777347.515156.771839080462190.73816091954
787425.755156.771839080462268.97816091954
797778.515156.771839080462621.73816091954
807822.335156.771839080462665.55816091954
818181.225156.771839080463024.44816091954
828371.475156.771839080463214.69816091954
838347.715156.771839080463190.93816091954
848672.115156.771839080463515.33816091954
858802.795156.771839080463646.01816091954
869138.465156.771839080463981.68816091954
879123.295156.771839080463966.51816091954
889023.217023.067142857142000.14285714286
898850.417023.067142857141827.34285714286
908864.587023.067142857141841.51285714286
919163.747023.067142857142140.67285714286
928516.667023.067142857141493.59285714286
938553.447023.067142857141530.37285714286
947555.27023.06714285714532.132857142857
957851.227023.06714285714828.152857142857
9674427023.06714285714418.932857142857
977992.537023.06714285714969.462857142857
988264.047023.067142857141240.97285714286
997517.397023.06714285714494.322857142857
1007200.47023.06714285714177.332857142857
1017193.697023.06714285714170.622857142857
1026193.587023.06714285714-829.487142857143
1035104.217023.06714285714-1918.85714285714
1044800.467023.06714285714-2222.60714285714
1054461.617023.06714285714-2561.45714285714
1064398.597023.06714285714-2624.47714285714
1074243.637023.06714285714-2779.43714285714
1084293.827023.06714285714-2729.24714285714

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4716.99 & 5156.77183908045 & -439.781839080449 \tabularnewline
2 & 4926.65 & 5156.77183908046 & -230.121839080459 \tabularnewline
3 & 4920.1 & 5156.77183908046 & -236.671839080460 \tabularnewline
4 & 5170.09 & 5156.77183908046 & 13.3181609195402 \tabularnewline
5 & 5246.24 & 5156.77183908046 & 89.4681609195398 \tabularnewline
6 & 5283.61 & 5156.77183908046 & 126.838160919540 \tabularnewline
7 & 4979.05 & 5156.77183908046 & -177.72183908046 \tabularnewline
8 & 4825.2 & 5156.77183908046 & -331.57183908046 \tabularnewline
9 & 4695.12 & 5156.77183908046 & -461.65183908046 \tabularnewline
10 & 4711.54 & 5156.77183908046 & -445.23183908046 \tabularnewline
11 & 4727.22 & 5156.77183908046 & -429.551839080460 \tabularnewline
12 & 4384.96 & 5156.77183908046 & -771.81183908046 \tabularnewline
13 & 4378.75 & 5156.77183908046 & -778.02183908046 \tabularnewline
14 & 4472.93 & 5156.77183908046 & -683.84183908046 \tabularnewline
15 & 4564.07 & 5156.77183908046 & -592.70183908046 \tabularnewline
16 & 4310.54 & 5156.77183908046 & -846.23183908046 \tabularnewline
17 & 4171.38 & 5156.77183908046 & -985.39183908046 \tabularnewline
18 & 4049.38 & 5156.77183908046 & -1107.39183908046 \tabularnewline
19 & 3591.37 & 5156.77183908046 & -1565.40183908046 \tabularnewline
20 & 3720.46 & 5156.77183908046 & -1436.31183908046 \tabularnewline
21 & 4107.23 & 5156.77183908046 & -1049.54183908046 \tabularnewline
22 & 4101.71 & 5156.77183908046 & -1055.06183908046 \tabularnewline
23 & 4162.34 & 5156.77183908046 & -994.43183908046 \tabularnewline
24 & 4136.22 & 5156.77183908046 & -1020.55183908046 \tabularnewline
25 & 4125.88 & 5156.77183908046 & -1030.89183908046 \tabularnewline
26 & 4031.48 & 5156.77183908046 & -1125.29183908046 \tabularnewline
27 & 3761.36 & 5156.77183908046 & -1395.41183908046 \tabularnewline
28 & 3408.56 & 5156.77183908046 & -1748.21183908046 \tabularnewline
29 & 3228.47 & 5156.77183908046 & -1928.30183908046 \tabularnewline
30 & 3090.45 & 5156.77183908046 & -2066.32183908046 \tabularnewline
31 & 2741.14 & 5156.77183908046 & -2415.63183908046 \tabularnewline
32 & 2980.44 & 5156.77183908046 & -2176.33183908046 \tabularnewline
33 & 3104.33 & 5156.77183908046 & -2052.44183908046 \tabularnewline
34 & 3181.57 & 5156.77183908046 & -1975.20183908046 \tabularnewline
35 & 2863.86 & 5156.77183908046 & -2292.91183908046 \tabularnewline
36 & 2898.01 & 5156.77183908046 & -2258.76183908046 \tabularnewline
37 & 3112.33 & 5156.77183908046 & -2044.44183908046 \tabularnewline
38 & 3254.33 & 5156.77183908046 & -1902.44183908046 \tabularnewline
39 & 3513.47 & 5156.77183908046 & -1643.30183908046 \tabularnewline
40 & 3587.61 & 5156.77183908046 & -1569.16183908046 \tabularnewline
41 & 3727.45 & 5156.77183908046 & -1429.32183908046 \tabularnewline
42 & 3793.34 & 5156.77183908046 & -1363.43183908046 \tabularnewline
43 & 3817.58 & 5156.77183908046 & -1339.19183908046 \tabularnewline
44 & 3845.13 & 5156.77183908046 & -1311.64183908046 \tabularnewline
45 & 3931.86 & 5156.77183908046 & -1224.91183908046 \tabularnewline
46 & 4197.52 & 5156.77183908046 & -959.25183908046 \tabularnewline
47 & 4307.13 & 5156.77183908046 & -849.64183908046 \tabularnewline
48 & 4229.43 & 5156.77183908046 & -927.34183908046 \tabularnewline
49 & 4362.28 & 5156.77183908046 & -794.49183908046 \tabularnewline
50 & 4217.34 & 5156.77183908046 & -939.43183908046 \tabularnewline
51 & 4361.28 & 5156.77183908046 & -795.49183908046 \tabularnewline
52 & 4327.74 & 5156.77183908046 & -829.03183908046 \tabularnewline
53 & 4417.65 & 5156.77183908046 & -739.12183908046 \tabularnewline
54 & 4557.68 & 5156.77183908046 & -599.09183908046 \tabularnewline
55 & 4650.35 & 5156.77183908046 & -506.42183908046 \tabularnewline
56 & 4967.18 & 5156.77183908046 & -189.591839080460 \tabularnewline
57 & 5123.42 & 5156.77183908046 & -33.3518390804599 \tabularnewline
58 & 5290.85 & 5156.77183908046 & 134.078160919540 \tabularnewline
59 & 5535.66 & 5156.77183908046 & 378.88816091954 \tabularnewline
60 & 5514.06 & 5156.77183908046 & 357.288160919540 \tabularnewline
61 & 5493.88 & 5156.77183908046 & 337.10816091954 \tabularnewline
62 & 5694.83 & 5156.77183908046 & 538.05816091954 \tabularnewline
63 & 5850.41 & 5156.77183908046 & 693.63816091954 \tabularnewline
64 & 6116.64 & 5156.77183908046 & 959.86816091954 \tabularnewline
65 & 6175 & 5156.77183908046 & 1018.22816091954 \tabularnewline
66 & 6513.58 & 5156.77183908046 & 1356.80816091954 \tabularnewline
67 & 6383.78 & 5156.77183908046 & 1227.00816091954 \tabularnewline
68 & 6673.66 & 5156.77183908046 & 1516.88816091954 \tabularnewline
69 & 6936.61 & 5156.77183908046 & 1779.83816091954 \tabularnewline
70 & 7300.68 & 5156.77183908046 & 2143.90816091954 \tabularnewline
71 & 7392.93 & 5156.77183908046 & 2236.15816091954 \tabularnewline
72 & 7497.31 & 5156.77183908046 & 2340.53816091954 \tabularnewline
73 & 7584.71 & 5156.77183908046 & 2427.93816091954 \tabularnewline
74 & 7160.79 & 5156.77183908046 & 2004.01816091954 \tabularnewline
75 & 7196.19 & 5156.77183908046 & 2039.41816091954 \tabularnewline
76 & 7245.63 & 5156.77183908046 & 2088.85816091954 \tabularnewline
77 & 7347.51 & 5156.77183908046 & 2190.73816091954 \tabularnewline
78 & 7425.75 & 5156.77183908046 & 2268.97816091954 \tabularnewline
79 & 7778.51 & 5156.77183908046 & 2621.73816091954 \tabularnewline
80 & 7822.33 & 5156.77183908046 & 2665.55816091954 \tabularnewline
81 & 8181.22 & 5156.77183908046 & 3024.44816091954 \tabularnewline
82 & 8371.47 & 5156.77183908046 & 3214.69816091954 \tabularnewline
83 & 8347.71 & 5156.77183908046 & 3190.93816091954 \tabularnewline
84 & 8672.11 & 5156.77183908046 & 3515.33816091954 \tabularnewline
85 & 8802.79 & 5156.77183908046 & 3646.01816091954 \tabularnewline
86 & 9138.46 & 5156.77183908046 & 3981.68816091954 \tabularnewline
87 & 9123.29 & 5156.77183908046 & 3966.51816091954 \tabularnewline
88 & 9023.21 & 7023.06714285714 & 2000.14285714286 \tabularnewline
89 & 8850.41 & 7023.06714285714 & 1827.34285714286 \tabularnewline
90 & 8864.58 & 7023.06714285714 & 1841.51285714286 \tabularnewline
91 & 9163.74 & 7023.06714285714 & 2140.67285714286 \tabularnewline
92 & 8516.66 & 7023.06714285714 & 1493.59285714286 \tabularnewline
93 & 8553.44 & 7023.06714285714 & 1530.37285714286 \tabularnewline
94 & 7555.2 & 7023.06714285714 & 532.132857142857 \tabularnewline
95 & 7851.22 & 7023.06714285714 & 828.152857142857 \tabularnewline
96 & 7442 & 7023.06714285714 & 418.932857142857 \tabularnewline
97 & 7992.53 & 7023.06714285714 & 969.462857142857 \tabularnewline
98 & 8264.04 & 7023.06714285714 & 1240.97285714286 \tabularnewline
99 & 7517.39 & 7023.06714285714 & 494.322857142857 \tabularnewline
100 & 7200.4 & 7023.06714285714 & 177.332857142857 \tabularnewline
101 & 7193.69 & 7023.06714285714 & 170.622857142857 \tabularnewline
102 & 6193.58 & 7023.06714285714 & -829.487142857143 \tabularnewline
103 & 5104.21 & 7023.06714285714 & -1918.85714285714 \tabularnewline
104 & 4800.46 & 7023.06714285714 & -2222.60714285714 \tabularnewline
105 & 4461.61 & 7023.06714285714 & -2561.45714285714 \tabularnewline
106 & 4398.59 & 7023.06714285714 & -2624.47714285714 \tabularnewline
107 & 4243.63 & 7023.06714285714 & -2779.43714285714 \tabularnewline
108 & 4293.82 & 7023.06714285714 & -2729.24714285714 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57761&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]5156.77183908045[/C][C]-439.781839080449[/C][/ROW]
[ROW][C]2[/C][C]4926.65[/C][C]5156.77183908046[/C][C]-230.121839080459[/C][/ROW]
[ROW][C]3[/C][C]4920.1[/C][C]5156.77183908046[/C][C]-236.671839080460[/C][/ROW]
[ROW][C]4[/C][C]5170.09[/C][C]5156.77183908046[/C][C]13.3181609195402[/C][/ROW]
[ROW][C]5[/C][C]5246.24[/C][C]5156.77183908046[/C][C]89.4681609195398[/C][/ROW]
[ROW][C]6[/C][C]5283.61[/C][C]5156.77183908046[/C][C]126.838160919540[/C][/ROW]
[ROW][C]7[/C][C]4979.05[/C][C]5156.77183908046[/C][C]-177.72183908046[/C][/ROW]
[ROW][C]8[/C][C]4825.2[/C][C]5156.77183908046[/C][C]-331.57183908046[/C][/ROW]
[ROW][C]9[/C][C]4695.12[/C][C]5156.77183908046[/C][C]-461.65183908046[/C][/ROW]
[ROW][C]10[/C][C]4711.54[/C][C]5156.77183908046[/C][C]-445.23183908046[/C][/ROW]
[ROW][C]11[/C][C]4727.22[/C][C]5156.77183908046[/C][C]-429.551839080460[/C][/ROW]
[ROW][C]12[/C][C]4384.96[/C][C]5156.77183908046[/C][C]-771.81183908046[/C][/ROW]
[ROW][C]13[/C][C]4378.75[/C][C]5156.77183908046[/C][C]-778.02183908046[/C][/ROW]
[ROW][C]14[/C][C]4472.93[/C][C]5156.77183908046[/C][C]-683.84183908046[/C][/ROW]
[ROW][C]15[/C][C]4564.07[/C][C]5156.77183908046[/C][C]-592.70183908046[/C][/ROW]
[ROW][C]16[/C][C]4310.54[/C][C]5156.77183908046[/C][C]-846.23183908046[/C][/ROW]
[ROW][C]17[/C][C]4171.38[/C][C]5156.77183908046[/C][C]-985.39183908046[/C][/ROW]
[ROW][C]18[/C][C]4049.38[/C][C]5156.77183908046[/C][C]-1107.39183908046[/C][/ROW]
[ROW][C]19[/C][C]3591.37[/C][C]5156.77183908046[/C][C]-1565.40183908046[/C][/ROW]
[ROW][C]20[/C][C]3720.46[/C][C]5156.77183908046[/C][C]-1436.31183908046[/C][/ROW]
[ROW][C]21[/C][C]4107.23[/C][C]5156.77183908046[/C][C]-1049.54183908046[/C][/ROW]
[ROW][C]22[/C][C]4101.71[/C][C]5156.77183908046[/C][C]-1055.06183908046[/C][/ROW]
[ROW][C]23[/C][C]4162.34[/C][C]5156.77183908046[/C][C]-994.43183908046[/C][/ROW]
[ROW][C]24[/C][C]4136.22[/C][C]5156.77183908046[/C][C]-1020.55183908046[/C][/ROW]
[ROW][C]25[/C][C]4125.88[/C][C]5156.77183908046[/C][C]-1030.89183908046[/C][/ROW]
[ROW][C]26[/C][C]4031.48[/C][C]5156.77183908046[/C][C]-1125.29183908046[/C][/ROW]
[ROW][C]27[/C][C]3761.36[/C][C]5156.77183908046[/C][C]-1395.41183908046[/C][/ROW]
[ROW][C]28[/C][C]3408.56[/C][C]5156.77183908046[/C][C]-1748.21183908046[/C][/ROW]
[ROW][C]29[/C][C]3228.47[/C][C]5156.77183908046[/C][C]-1928.30183908046[/C][/ROW]
[ROW][C]30[/C][C]3090.45[/C][C]5156.77183908046[/C][C]-2066.32183908046[/C][/ROW]
[ROW][C]31[/C][C]2741.14[/C][C]5156.77183908046[/C][C]-2415.63183908046[/C][/ROW]
[ROW][C]32[/C][C]2980.44[/C][C]5156.77183908046[/C][C]-2176.33183908046[/C][/ROW]
[ROW][C]33[/C][C]3104.33[/C][C]5156.77183908046[/C][C]-2052.44183908046[/C][/ROW]
[ROW][C]34[/C][C]3181.57[/C][C]5156.77183908046[/C][C]-1975.20183908046[/C][/ROW]
[ROW][C]35[/C][C]2863.86[/C][C]5156.77183908046[/C][C]-2292.91183908046[/C][/ROW]
[ROW][C]36[/C][C]2898.01[/C][C]5156.77183908046[/C][C]-2258.76183908046[/C][/ROW]
[ROW][C]37[/C][C]3112.33[/C][C]5156.77183908046[/C][C]-2044.44183908046[/C][/ROW]
[ROW][C]38[/C][C]3254.33[/C][C]5156.77183908046[/C][C]-1902.44183908046[/C][/ROW]
[ROW][C]39[/C][C]3513.47[/C][C]5156.77183908046[/C][C]-1643.30183908046[/C][/ROW]
[ROW][C]40[/C][C]3587.61[/C][C]5156.77183908046[/C][C]-1569.16183908046[/C][/ROW]
[ROW][C]41[/C][C]3727.45[/C][C]5156.77183908046[/C][C]-1429.32183908046[/C][/ROW]
[ROW][C]42[/C][C]3793.34[/C][C]5156.77183908046[/C][C]-1363.43183908046[/C][/ROW]
[ROW][C]43[/C][C]3817.58[/C][C]5156.77183908046[/C][C]-1339.19183908046[/C][/ROW]
[ROW][C]44[/C][C]3845.13[/C][C]5156.77183908046[/C][C]-1311.64183908046[/C][/ROW]
[ROW][C]45[/C][C]3931.86[/C][C]5156.77183908046[/C][C]-1224.91183908046[/C][/ROW]
[ROW][C]46[/C][C]4197.52[/C][C]5156.77183908046[/C][C]-959.25183908046[/C][/ROW]
[ROW][C]47[/C][C]4307.13[/C][C]5156.77183908046[/C][C]-849.64183908046[/C][/ROW]
[ROW][C]48[/C][C]4229.43[/C][C]5156.77183908046[/C][C]-927.34183908046[/C][/ROW]
[ROW][C]49[/C][C]4362.28[/C][C]5156.77183908046[/C][C]-794.49183908046[/C][/ROW]
[ROW][C]50[/C][C]4217.34[/C][C]5156.77183908046[/C][C]-939.43183908046[/C][/ROW]
[ROW][C]51[/C][C]4361.28[/C][C]5156.77183908046[/C][C]-795.49183908046[/C][/ROW]
[ROW][C]52[/C][C]4327.74[/C][C]5156.77183908046[/C][C]-829.03183908046[/C][/ROW]
[ROW][C]53[/C][C]4417.65[/C][C]5156.77183908046[/C][C]-739.12183908046[/C][/ROW]
[ROW][C]54[/C][C]4557.68[/C][C]5156.77183908046[/C][C]-599.09183908046[/C][/ROW]
[ROW][C]55[/C][C]4650.35[/C][C]5156.77183908046[/C][C]-506.42183908046[/C][/ROW]
[ROW][C]56[/C][C]4967.18[/C][C]5156.77183908046[/C][C]-189.591839080460[/C][/ROW]
[ROW][C]57[/C][C]5123.42[/C][C]5156.77183908046[/C][C]-33.3518390804599[/C][/ROW]
[ROW][C]58[/C][C]5290.85[/C][C]5156.77183908046[/C][C]134.078160919540[/C][/ROW]
[ROW][C]59[/C][C]5535.66[/C][C]5156.77183908046[/C][C]378.88816091954[/C][/ROW]
[ROW][C]60[/C][C]5514.06[/C][C]5156.77183908046[/C][C]357.288160919540[/C][/ROW]
[ROW][C]61[/C][C]5493.88[/C][C]5156.77183908046[/C][C]337.10816091954[/C][/ROW]
[ROW][C]62[/C][C]5694.83[/C][C]5156.77183908046[/C][C]538.05816091954[/C][/ROW]
[ROW][C]63[/C][C]5850.41[/C][C]5156.77183908046[/C][C]693.63816091954[/C][/ROW]
[ROW][C]64[/C][C]6116.64[/C][C]5156.77183908046[/C][C]959.86816091954[/C][/ROW]
[ROW][C]65[/C][C]6175[/C][C]5156.77183908046[/C][C]1018.22816091954[/C][/ROW]
[ROW][C]66[/C][C]6513.58[/C][C]5156.77183908046[/C][C]1356.80816091954[/C][/ROW]
[ROW][C]67[/C][C]6383.78[/C][C]5156.77183908046[/C][C]1227.00816091954[/C][/ROW]
[ROW][C]68[/C][C]6673.66[/C][C]5156.77183908046[/C][C]1516.88816091954[/C][/ROW]
[ROW][C]69[/C][C]6936.61[/C][C]5156.77183908046[/C][C]1779.83816091954[/C][/ROW]
[ROW][C]70[/C][C]7300.68[/C][C]5156.77183908046[/C][C]2143.90816091954[/C][/ROW]
[ROW][C]71[/C][C]7392.93[/C][C]5156.77183908046[/C][C]2236.15816091954[/C][/ROW]
[ROW][C]72[/C][C]7497.31[/C][C]5156.77183908046[/C][C]2340.53816091954[/C][/ROW]
[ROW][C]73[/C][C]7584.71[/C][C]5156.77183908046[/C][C]2427.93816091954[/C][/ROW]
[ROW][C]74[/C][C]7160.79[/C][C]5156.77183908046[/C][C]2004.01816091954[/C][/ROW]
[ROW][C]75[/C][C]7196.19[/C][C]5156.77183908046[/C][C]2039.41816091954[/C][/ROW]
[ROW][C]76[/C][C]7245.63[/C][C]5156.77183908046[/C][C]2088.85816091954[/C][/ROW]
[ROW][C]77[/C][C]7347.51[/C][C]5156.77183908046[/C][C]2190.73816091954[/C][/ROW]
[ROW][C]78[/C][C]7425.75[/C][C]5156.77183908046[/C][C]2268.97816091954[/C][/ROW]
[ROW][C]79[/C][C]7778.51[/C][C]5156.77183908046[/C][C]2621.73816091954[/C][/ROW]
[ROW][C]80[/C][C]7822.33[/C][C]5156.77183908046[/C][C]2665.55816091954[/C][/ROW]
[ROW][C]81[/C][C]8181.22[/C][C]5156.77183908046[/C][C]3024.44816091954[/C][/ROW]
[ROW][C]82[/C][C]8371.47[/C][C]5156.77183908046[/C][C]3214.69816091954[/C][/ROW]
[ROW][C]83[/C][C]8347.71[/C][C]5156.77183908046[/C][C]3190.93816091954[/C][/ROW]
[ROW][C]84[/C][C]8672.11[/C][C]5156.77183908046[/C][C]3515.33816091954[/C][/ROW]
[ROW][C]85[/C][C]8802.79[/C][C]5156.77183908046[/C][C]3646.01816091954[/C][/ROW]
[ROW][C]86[/C][C]9138.46[/C][C]5156.77183908046[/C][C]3981.68816091954[/C][/ROW]
[ROW][C]87[/C][C]9123.29[/C][C]5156.77183908046[/C][C]3966.51816091954[/C][/ROW]
[ROW][C]88[/C][C]9023.21[/C][C]7023.06714285714[/C][C]2000.14285714286[/C][/ROW]
[ROW][C]89[/C][C]8850.41[/C][C]7023.06714285714[/C][C]1827.34285714286[/C][/ROW]
[ROW][C]90[/C][C]8864.58[/C][C]7023.06714285714[/C][C]1841.51285714286[/C][/ROW]
[ROW][C]91[/C][C]9163.74[/C][C]7023.06714285714[/C][C]2140.67285714286[/C][/ROW]
[ROW][C]92[/C][C]8516.66[/C][C]7023.06714285714[/C][C]1493.59285714286[/C][/ROW]
[ROW][C]93[/C][C]8553.44[/C][C]7023.06714285714[/C][C]1530.37285714286[/C][/ROW]
[ROW][C]94[/C][C]7555.2[/C][C]7023.06714285714[/C][C]532.132857142857[/C][/ROW]
[ROW][C]95[/C][C]7851.22[/C][C]7023.06714285714[/C][C]828.152857142857[/C][/ROW]
[ROW][C]96[/C][C]7442[/C][C]7023.06714285714[/C][C]418.932857142857[/C][/ROW]
[ROW][C]97[/C][C]7992.53[/C][C]7023.06714285714[/C][C]969.462857142857[/C][/ROW]
[ROW][C]98[/C][C]8264.04[/C][C]7023.06714285714[/C][C]1240.97285714286[/C][/ROW]
[ROW][C]99[/C][C]7517.39[/C][C]7023.06714285714[/C][C]494.322857142857[/C][/ROW]
[ROW][C]100[/C][C]7200.4[/C][C]7023.06714285714[/C][C]177.332857142857[/C][/ROW]
[ROW][C]101[/C][C]7193.69[/C][C]7023.06714285714[/C][C]170.622857142857[/C][/ROW]
[ROW][C]102[/C][C]6193.58[/C][C]7023.06714285714[/C][C]-829.487142857143[/C][/ROW]
[ROW][C]103[/C][C]5104.21[/C][C]7023.06714285714[/C][C]-1918.85714285714[/C][/ROW]
[ROW][C]104[/C][C]4800.46[/C][C]7023.06714285714[/C][C]-2222.60714285714[/C][/ROW]
[ROW][C]105[/C][C]4461.61[/C][C]7023.06714285714[/C][C]-2561.45714285714[/C][/ROW]
[ROW][C]106[/C][C]4398.59[/C][C]7023.06714285714[/C][C]-2624.47714285714[/C][/ROW]
[ROW][C]107[/C][C]4243.63[/C][C]7023.06714285714[/C][C]-2779.43714285714[/C][/ROW]
[ROW][C]108[/C][C]4293.82[/C][C]7023.06714285714[/C][C]-2729.24714285714[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57761&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57761&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
14716.995156.77183908045-439.781839080449
24926.655156.77183908046-230.121839080459
34920.15156.77183908046-236.671839080460
45170.095156.7718390804613.3181609195402
55246.245156.7718390804689.4681609195398
65283.615156.77183908046126.838160919540
74979.055156.77183908046-177.72183908046
84825.25156.77183908046-331.57183908046
94695.125156.77183908046-461.65183908046
104711.545156.77183908046-445.23183908046
114727.225156.77183908046-429.551839080460
124384.965156.77183908046-771.81183908046
134378.755156.77183908046-778.02183908046
144472.935156.77183908046-683.84183908046
154564.075156.77183908046-592.70183908046
164310.545156.77183908046-846.23183908046
174171.385156.77183908046-985.39183908046
184049.385156.77183908046-1107.39183908046
193591.375156.77183908046-1565.40183908046
203720.465156.77183908046-1436.31183908046
214107.235156.77183908046-1049.54183908046
224101.715156.77183908046-1055.06183908046
234162.345156.77183908046-994.43183908046
244136.225156.77183908046-1020.55183908046
254125.885156.77183908046-1030.89183908046
264031.485156.77183908046-1125.29183908046
273761.365156.77183908046-1395.41183908046
283408.565156.77183908046-1748.21183908046
293228.475156.77183908046-1928.30183908046
303090.455156.77183908046-2066.32183908046
312741.145156.77183908046-2415.63183908046
322980.445156.77183908046-2176.33183908046
333104.335156.77183908046-2052.44183908046
343181.575156.77183908046-1975.20183908046
352863.865156.77183908046-2292.91183908046
362898.015156.77183908046-2258.76183908046
373112.335156.77183908046-2044.44183908046
383254.335156.77183908046-1902.44183908046
393513.475156.77183908046-1643.30183908046
403587.615156.77183908046-1569.16183908046
413727.455156.77183908046-1429.32183908046
423793.345156.77183908046-1363.43183908046
433817.585156.77183908046-1339.19183908046
443845.135156.77183908046-1311.64183908046
453931.865156.77183908046-1224.91183908046
464197.525156.77183908046-959.25183908046
474307.135156.77183908046-849.64183908046
484229.435156.77183908046-927.34183908046
494362.285156.77183908046-794.49183908046
504217.345156.77183908046-939.43183908046
514361.285156.77183908046-795.49183908046
524327.745156.77183908046-829.03183908046
534417.655156.77183908046-739.12183908046
544557.685156.77183908046-599.09183908046
554650.355156.77183908046-506.42183908046
564967.185156.77183908046-189.591839080460
575123.425156.77183908046-33.3518390804599
585290.855156.77183908046134.078160919540
595535.665156.77183908046378.88816091954
605514.065156.77183908046357.288160919540
615493.885156.77183908046337.10816091954
625694.835156.77183908046538.05816091954
635850.415156.77183908046693.63816091954
646116.645156.77183908046959.86816091954
6561755156.771839080461018.22816091954
666513.585156.771839080461356.80816091954
676383.785156.771839080461227.00816091954
686673.665156.771839080461516.88816091954
696936.615156.771839080461779.83816091954
707300.685156.771839080462143.90816091954
717392.935156.771839080462236.15816091954
727497.315156.771839080462340.53816091954
737584.715156.771839080462427.93816091954
747160.795156.771839080462004.01816091954
757196.195156.771839080462039.41816091954
767245.635156.771839080462088.85816091954
777347.515156.771839080462190.73816091954
787425.755156.771839080462268.97816091954
797778.515156.771839080462621.73816091954
807822.335156.771839080462665.55816091954
818181.225156.771839080463024.44816091954
828371.475156.771839080463214.69816091954
838347.715156.771839080463190.93816091954
848672.115156.771839080463515.33816091954
858802.795156.771839080463646.01816091954
869138.465156.771839080463981.68816091954
879123.295156.771839080463966.51816091954
889023.217023.067142857142000.14285714286
898850.417023.067142857141827.34285714286
908864.587023.067142857141841.51285714286
919163.747023.067142857142140.67285714286
928516.667023.067142857141493.59285714286
938553.447023.067142857141530.37285714286
947555.27023.06714285714532.132857142857
957851.227023.06714285714828.152857142857
9674427023.06714285714418.932857142857
977992.537023.06714285714969.462857142857
988264.047023.067142857141240.97285714286
997517.397023.06714285714494.322857142857
1007200.47023.06714285714177.332857142857
1017193.697023.06714285714170.622857142857
1026193.587023.06714285714-829.487142857143
1035104.217023.06714285714-1918.85714285714
1044800.467023.06714285714-2222.60714285714
1054461.617023.06714285714-2561.45714285714
1064398.597023.06714285714-2624.47714285714
1074243.637023.06714285714-2779.43714285714
1084293.827023.06714285714-2729.24714285714







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.003865875398638120.007731750797276230.996134124601362
60.000833192642823470.001666385285646940.999166807357177
70.0001042053944688020.0002084107889376050.999895794605531
81.76408811240380e-053.52817622480760e-050.999982359118876
94.83299699929965e-069.6659939985993e-060.999995167003001
101.03730720163209e-062.07461440326419e-060.999998962692798
111.9017653217698e-073.8035306435396e-070.999999809823468
122.00468676949373e-074.00937353898745e-070.999999799531323
131.22150316652121e-072.44300633304242e-070.999999877849683
143.8681971028116e-087.7363942056232e-080.99999996131803
158.5218669285961e-091.70437338571922e-080.999999991478133
164.41973977526035e-098.8394795505207e-090.99999999558026
173.53411828546817e-097.06823657093634e-090.999999996465882
183.69187023893153e-097.38374047786306e-090.99999999630813
192.35486569933385e-084.70973139866771e-080.999999976451343
203.51800137012001e-087.03600274024001e-080.999999964819986
211.44502534467465e-082.8900506893493e-080.999999985549747
225.7655327651551e-091.15310655303102e-080.999999994234467
232.00116463288809e-094.00232926577618e-090.999999997998835
247.05926352202411e-101.41185270440482e-090.999999999294074
252.46950405100206e-104.93900810200411e-100.99999999975305
261.00413978200115e-102.00827956400231e-100.999999999899586
277.73114166708188e-111.54622833341638e-100.999999999922689
281.7140715224743e-103.4281430449486e-100.999999999828593
295.327347041672e-101.0654694083344e-090.999999999467265
301.85492649864778e-093.70985299729557e-090.999999998145074
311.40870654481176e-082.81741308962351e-080.999999985912934
323.35277371633315e-086.70554743266631e-080.999999966472263
335.15867452475614e-081.03173490495123e-070.999999948413255
346.39256096138433e-081.27851219227687e-070.99999993607439
351.45627018457859e-072.91254036915719e-070.999999854372982
362.84062364311938e-075.68124728623876e-070.999999715937636
373.65361132474051e-077.30722264948103e-070.999999634638868
383.85621805641011e-077.71243611282022e-070.999999614378194
393.06762615661152e-076.13525231322305e-070.999999693237384
402.35309129812231e-074.70618259624462e-070.99999976469087
411.66512336136506e-073.33024672273013e-070.999999833487664
421.1690093419261e-072.3380186838522e-070.999999883099066
438.43651206713238e-081.68730241342648e-070.99999991563488
446.28265197535801e-081.25653039507160e-070.99999993717348
454.68115822839482e-089.36231645678964e-080.999999953188418
463.32534873483773e-086.65069746967545e-080.999999966746513
472.44525035962369e-084.89050071924739e-080.999999975547496
481.90830671003401e-083.81661342006802e-080.999999980916933
491.55759856972772e-083.11519713945544e-080.999999984424014
501.39210980960704e-082.78421961921409e-080.999999986078902
511.30911604925300e-082.61823209850601e-080.99999998690884
521.35130485479267e-082.70260970958534e-080.999999986486951
531.51758859736619e-083.03517719473238e-080.999999984824114
541.87734261297878e-083.75468522595755e-080.999999981226574
552.59113568543129e-085.18227137086258e-080.999999974088643
564.38888268330831e-088.77776536661662e-080.999999956111173
578.63113297212975e-081.72622659442595e-070.99999991368867
581.97184110097417e-073.94368220194834e-070.99999980281589
595.53526236840371e-071.10705247368074e-060.999999446473763
601.42112839320398e-062.84225678640796e-060.999998578871607
613.4747544798025e-066.949508959605e-060.99999652524552
629.44636004492853e-061.88927200898571e-050.999990553639955
632.67072750991714e-055.34145501983428e-050.9999732927249
648.23418052466462e-050.0001646836104932920.999917658194753
650.0002270436635612890.0004540873271225780.999772956336439
660.0006727166916356650.001345433383271330.999327283308364
670.001534612818464010.003069225636928020.998465387181536
680.003526145150320680.007052290300641360.99647385484968
690.007803057930738970.01560611586147790.99219694206926
700.01713642626517280.03427285253034570.982863573734827
710.03185807676102960.06371615352205920.96814192323897
720.05230359363313410.1046071872662680.947696406366866
730.07742082246178410.1548416449235680.922579177538216
740.09560970892052350.1912194178410470.904390291079477
750.1143199333882100.2286398667764200.88568006661179
760.1334747192186960.2669494384373920.866525280781304
770.1533915879347110.3067831758694210.84660841206529
780.1736325813586040.3472651627172080.826367418641396
790.1969626517034670.3939253034069340.803037348296533
800.218143194079730.436286388159460.78185680592027
810.2423722223346770.4847444446693540.757627777665323
820.2654279680781790.5308559361563580.734572031921821
830.2815937642563940.5631875285127890.718406235743606
840.2994291262142820.5988582524285630.700570873785718
850.3131757819838260.6263515639676520.686824218016174
860.3291194863596280.6582389727192550.670880513640372
870.3348593332525340.6697186665050680.665140666747466
880.3408177066386820.6816354132773650.659182293361318
890.343658747102040.687317494204080.65634125289796
900.3582291122062680.7164582244125360.641770887793732
910.4211014847401380.8422029694802760.578898515259862
920.4393922612156780.8787845224313560.560607738784322
930.4809239028894510.9618478057789010.519076097110549
940.446285499643590.892570999287180.55371450035641
950.4422065055143920.8844130110287840.557793494485608
960.4131911763834950.826382352766990.586808823616505
970.4601276063764810.9202552127529620.539872393623519
980.6134264522134270.7731470955731460.386573547786573
990.7000716296746540.5998567406506920.299928370325346
1000.7909802111696570.4180395776606850.209019788830343
1010.9497352113818680.1005295772362650.0502647886181324
1020.9942385346889440.0115229306221130.0057614653110565
1030.9947121068241880.01057578635162450.00528789317581225

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.00386587539863812 & 0.00773175079727623 & 0.996134124601362 \tabularnewline
6 & 0.00083319264282347 & 0.00166638528564694 & 0.999166807357177 \tabularnewline
7 & 0.000104205394468802 & 0.000208410788937605 & 0.999895794605531 \tabularnewline
8 & 1.76408811240380e-05 & 3.52817622480760e-05 & 0.999982359118876 \tabularnewline
9 & 4.83299699929965e-06 & 9.6659939985993e-06 & 0.999995167003001 \tabularnewline
10 & 1.03730720163209e-06 & 2.07461440326419e-06 & 0.999998962692798 \tabularnewline
11 & 1.9017653217698e-07 & 3.8035306435396e-07 & 0.999999809823468 \tabularnewline
12 & 2.00468676949373e-07 & 4.00937353898745e-07 & 0.999999799531323 \tabularnewline
13 & 1.22150316652121e-07 & 2.44300633304242e-07 & 0.999999877849683 \tabularnewline
14 & 3.8681971028116e-08 & 7.7363942056232e-08 & 0.99999996131803 \tabularnewline
15 & 8.5218669285961e-09 & 1.70437338571922e-08 & 0.999999991478133 \tabularnewline
16 & 4.41973977526035e-09 & 8.8394795505207e-09 & 0.99999999558026 \tabularnewline
17 & 3.53411828546817e-09 & 7.06823657093634e-09 & 0.999999996465882 \tabularnewline
18 & 3.69187023893153e-09 & 7.38374047786306e-09 & 0.99999999630813 \tabularnewline
19 & 2.35486569933385e-08 & 4.70973139866771e-08 & 0.999999976451343 \tabularnewline
20 & 3.51800137012001e-08 & 7.03600274024001e-08 & 0.999999964819986 \tabularnewline
21 & 1.44502534467465e-08 & 2.8900506893493e-08 & 0.999999985549747 \tabularnewline
22 & 5.7655327651551e-09 & 1.15310655303102e-08 & 0.999999994234467 \tabularnewline
23 & 2.00116463288809e-09 & 4.00232926577618e-09 & 0.999999997998835 \tabularnewline
24 & 7.05926352202411e-10 & 1.41185270440482e-09 & 0.999999999294074 \tabularnewline
25 & 2.46950405100206e-10 & 4.93900810200411e-10 & 0.99999999975305 \tabularnewline
26 & 1.00413978200115e-10 & 2.00827956400231e-10 & 0.999999999899586 \tabularnewline
27 & 7.73114166708188e-11 & 1.54622833341638e-10 & 0.999999999922689 \tabularnewline
28 & 1.7140715224743e-10 & 3.4281430449486e-10 & 0.999999999828593 \tabularnewline
29 & 5.327347041672e-10 & 1.0654694083344e-09 & 0.999999999467265 \tabularnewline
30 & 1.85492649864778e-09 & 3.70985299729557e-09 & 0.999999998145074 \tabularnewline
31 & 1.40870654481176e-08 & 2.81741308962351e-08 & 0.999999985912934 \tabularnewline
32 & 3.35277371633315e-08 & 6.70554743266631e-08 & 0.999999966472263 \tabularnewline
33 & 5.15867452475614e-08 & 1.03173490495123e-07 & 0.999999948413255 \tabularnewline
34 & 6.39256096138433e-08 & 1.27851219227687e-07 & 0.99999993607439 \tabularnewline
35 & 1.45627018457859e-07 & 2.91254036915719e-07 & 0.999999854372982 \tabularnewline
36 & 2.84062364311938e-07 & 5.68124728623876e-07 & 0.999999715937636 \tabularnewline
37 & 3.65361132474051e-07 & 7.30722264948103e-07 & 0.999999634638868 \tabularnewline
38 & 3.85621805641011e-07 & 7.71243611282022e-07 & 0.999999614378194 \tabularnewline
39 & 3.06762615661152e-07 & 6.13525231322305e-07 & 0.999999693237384 \tabularnewline
40 & 2.35309129812231e-07 & 4.70618259624462e-07 & 0.99999976469087 \tabularnewline
41 & 1.66512336136506e-07 & 3.33024672273013e-07 & 0.999999833487664 \tabularnewline
42 & 1.1690093419261e-07 & 2.3380186838522e-07 & 0.999999883099066 \tabularnewline
43 & 8.43651206713238e-08 & 1.68730241342648e-07 & 0.99999991563488 \tabularnewline
44 & 6.28265197535801e-08 & 1.25653039507160e-07 & 0.99999993717348 \tabularnewline
45 & 4.68115822839482e-08 & 9.36231645678964e-08 & 0.999999953188418 \tabularnewline
46 & 3.32534873483773e-08 & 6.65069746967545e-08 & 0.999999966746513 \tabularnewline
47 & 2.44525035962369e-08 & 4.89050071924739e-08 & 0.999999975547496 \tabularnewline
48 & 1.90830671003401e-08 & 3.81661342006802e-08 & 0.999999980916933 \tabularnewline
49 & 1.55759856972772e-08 & 3.11519713945544e-08 & 0.999999984424014 \tabularnewline
50 & 1.39210980960704e-08 & 2.78421961921409e-08 & 0.999999986078902 \tabularnewline
51 & 1.30911604925300e-08 & 2.61823209850601e-08 & 0.99999998690884 \tabularnewline
52 & 1.35130485479267e-08 & 2.70260970958534e-08 & 0.999999986486951 \tabularnewline
53 & 1.51758859736619e-08 & 3.03517719473238e-08 & 0.999999984824114 \tabularnewline
54 & 1.87734261297878e-08 & 3.75468522595755e-08 & 0.999999981226574 \tabularnewline
55 & 2.59113568543129e-08 & 5.18227137086258e-08 & 0.999999974088643 \tabularnewline
56 & 4.38888268330831e-08 & 8.77776536661662e-08 & 0.999999956111173 \tabularnewline
57 & 8.63113297212975e-08 & 1.72622659442595e-07 & 0.99999991368867 \tabularnewline
58 & 1.97184110097417e-07 & 3.94368220194834e-07 & 0.99999980281589 \tabularnewline
59 & 5.53526236840371e-07 & 1.10705247368074e-06 & 0.999999446473763 \tabularnewline
60 & 1.42112839320398e-06 & 2.84225678640796e-06 & 0.999998578871607 \tabularnewline
61 & 3.4747544798025e-06 & 6.949508959605e-06 & 0.99999652524552 \tabularnewline
62 & 9.44636004492853e-06 & 1.88927200898571e-05 & 0.999990553639955 \tabularnewline
63 & 2.67072750991714e-05 & 5.34145501983428e-05 & 0.9999732927249 \tabularnewline
64 & 8.23418052466462e-05 & 0.000164683610493292 & 0.999917658194753 \tabularnewline
65 & 0.000227043663561289 & 0.000454087327122578 & 0.999772956336439 \tabularnewline
66 & 0.000672716691635665 & 0.00134543338327133 & 0.999327283308364 \tabularnewline
67 & 0.00153461281846401 & 0.00306922563692802 & 0.998465387181536 \tabularnewline
68 & 0.00352614515032068 & 0.00705229030064136 & 0.99647385484968 \tabularnewline
69 & 0.00780305793073897 & 0.0156061158614779 & 0.99219694206926 \tabularnewline
70 & 0.0171364262651728 & 0.0342728525303457 & 0.982863573734827 \tabularnewline
71 & 0.0318580767610296 & 0.0637161535220592 & 0.96814192323897 \tabularnewline
72 & 0.0523035936331341 & 0.104607187266268 & 0.947696406366866 \tabularnewline
73 & 0.0774208224617841 & 0.154841644923568 & 0.922579177538216 \tabularnewline
74 & 0.0956097089205235 & 0.191219417841047 & 0.904390291079477 \tabularnewline
75 & 0.114319933388210 & 0.228639866776420 & 0.88568006661179 \tabularnewline
76 & 0.133474719218696 & 0.266949438437392 & 0.866525280781304 \tabularnewline
77 & 0.153391587934711 & 0.306783175869421 & 0.84660841206529 \tabularnewline
78 & 0.173632581358604 & 0.347265162717208 & 0.826367418641396 \tabularnewline
79 & 0.196962651703467 & 0.393925303406934 & 0.803037348296533 \tabularnewline
80 & 0.21814319407973 & 0.43628638815946 & 0.78185680592027 \tabularnewline
81 & 0.242372222334677 & 0.484744444669354 & 0.757627777665323 \tabularnewline
82 & 0.265427968078179 & 0.530855936156358 & 0.734572031921821 \tabularnewline
83 & 0.281593764256394 & 0.563187528512789 & 0.718406235743606 \tabularnewline
84 & 0.299429126214282 & 0.598858252428563 & 0.700570873785718 \tabularnewline
85 & 0.313175781983826 & 0.626351563967652 & 0.686824218016174 \tabularnewline
86 & 0.329119486359628 & 0.658238972719255 & 0.670880513640372 \tabularnewline
87 & 0.334859333252534 & 0.669718666505068 & 0.665140666747466 \tabularnewline
88 & 0.340817706638682 & 0.681635413277365 & 0.659182293361318 \tabularnewline
89 & 0.34365874710204 & 0.68731749420408 & 0.65634125289796 \tabularnewline
90 & 0.358229112206268 & 0.716458224412536 & 0.641770887793732 \tabularnewline
91 & 0.421101484740138 & 0.842202969480276 & 0.578898515259862 \tabularnewline
92 & 0.439392261215678 & 0.878784522431356 & 0.560607738784322 \tabularnewline
93 & 0.480923902889451 & 0.961847805778901 & 0.519076097110549 \tabularnewline
94 & 0.44628549964359 & 0.89257099928718 & 0.55371450035641 \tabularnewline
95 & 0.442206505514392 & 0.884413011028784 & 0.557793494485608 \tabularnewline
96 & 0.413191176383495 & 0.82638235276699 & 0.586808823616505 \tabularnewline
97 & 0.460127606376481 & 0.920255212752962 & 0.539872393623519 \tabularnewline
98 & 0.613426452213427 & 0.773147095573146 & 0.386573547786573 \tabularnewline
99 & 0.700071629674654 & 0.599856740650692 & 0.299928370325346 \tabularnewline
100 & 0.790980211169657 & 0.418039577660685 & 0.209019788830343 \tabularnewline
101 & 0.949735211381868 & 0.100529577236265 & 0.0502647886181324 \tabularnewline
102 & 0.994238534688944 & 0.011522930622113 & 0.0057614653110565 \tabularnewline
103 & 0.994712106824188 & 0.0105757863516245 & 0.00528789317581225 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57761&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]5[/C][C]0.00386587539863812[/C][C]0.00773175079727623[/C][C]0.996134124601362[/C][/ROW]
[ROW][C]6[/C][C]0.00083319264282347[/C][C]0.00166638528564694[/C][C]0.999166807357177[/C][/ROW]
[ROW][C]7[/C][C]0.000104205394468802[/C][C]0.000208410788937605[/C][C]0.999895794605531[/C][/ROW]
[ROW][C]8[/C][C]1.76408811240380e-05[/C][C]3.52817622480760e-05[/C][C]0.999982359118876[/C][/ROW]
[ROW][C]9[/C][C]4.83299699929965e-06[/C][C]9.6659939985993e-06[/C][C]0.999995167003001[/C][/ROW]
[ROW][C]10[/C][C]1.03730720163209e-06[/C][C]2.07461440326419e-06[/C][C]0.999998962692798[/C][/ROW]
[ROW][C]11[/C][C]1.9017653217698e-07[/C][C]3.8035306435396e-07[/C][C]0.999999809823468[/C][/ROW]
[ROW][C]12[/C][C]2.00468676949373e-07[/C][C]4.00937353898745e-07[/C][C]0.999999799531323[/C][/ROW]
[ROW][C]13[/C][C]1.22150316652121e-07[/C][C]2.44300633304242e-07[/C][C]0.999999877849683[/C][/ROW]
[ROW][C]14[/C][C]3.8681971028116e-08[/C][C]7.7363942056232e-08[/C][C]0.99999996131803[/C][/ROW]
[ROW][C]15[/C][C]8.5218669285961e-09[/C][C]1.70437338571922e-08[/C][C]0.999999991478133[/C][/ROW]
[ROW][C]16[/C][C]4.41973977526035e-09[/C][C]8.8394795505207e-09[/C][C]0.99999999558026[/C][/ROW]
[ROW][C]17[/C][C]3.53411828546817e-09[/C][C]7.06823657093634e-09[/C][C]0.999999996465882[/C][/ROW]
[ROW][C]18[/C][C]3.69187023893153e-09[/C][C]7.38374047786306e-09[/C][C]0.99999999630813[/C][/ROW]
[ROW][C]19[/C][C]2.35486569933385e-08[/C][C]4.70973139866771e-08[/C][C]0.999999976451343[/C][/ROW]
[ROW][C]20[/C][C]3.51800137012001e-08[/C][C]7.03600274024001e-08[/C][C]0.999999964819986[/C][/ROW]
[ROW][C]21[/C][C]1.44502534467465e-08[/C][C]2.8900506893493e-08[/C][C]0.999999985549747[/C][/ROW]
[ROW][C]22[/C][C]5.7655327651551e-09[/C][C]1.15310655303102e-08[/C][C]0.999999994234467[/C][/ROW]
[ROW][C]23[/C][C]2.00116463288809e-09[/C][C]4.00232926577618e-09[/C][C]0.999999997998835[/C][/ROW]
[ROW][C]24[/C][C]7.05926352202411e-10[/C][C]1.41185270440482e-09[/C][C]0.999999999294074[/C][/ROW]
[ROW][C]25[/C][C]2.46950405100206e-10[/C][C]4.93900810200411e-10[/C][C]0.99999999975305[/C][/ROW]
[ROW][C]26[/C][C]1.00413978200115e-10[/C][C]2.00827956400231e-10[/C][C]0.999999999899586[/C][/ROW]
[ROW][C]27[/C][C]7.73114166708188e-11[/C][C]1.54622833341638e-10[/C][C]0.999999999922689[/C][/ROW]
[ROW][C]28[/C][C]1.7140715224743e-10[/C][C]3.4281430449486e-10[/C][C]0.999999999828593[/C][/ROW]
[ROW][C]29[/C][C]5.327347041672e-10[/C][C]1.0654694083344e-09[/C][C]0.999999999467265[/C][/ROW]
[ROW][C]30[/C][C]1.85492649864778e-09[/C][C]3.70985299729557e-09[/C][C]0.999999998145074[/C][/ROW]
[ROW][C]31[/C][C]1.40870654481176e-08[/C][C]2.81741308962351e-08[/C][C]0.999999985912934[/C][/ROW]
[ROW][C]32[/C][C]3.35277371633315e-08[/C][C]6.70554743266631e-08[/C][C]0.999999966472263[/C][/ROW]
[ROW][C]33[/C][C]5.15867452475614e-08[/C][C]1.03173490495123e-07[/C][C]0.999999948413255[/C][/ROW]
[ROW][C]34[/C][C]6.39256096138433e-08[/C][C]1.27851219227687e-07[/C][C]0.99999993607439[/C][/ROW]
[ROW][C]35[/C][C]1.45627018457859e-07[/C][C]2.91254036915719e-07[/C][C]0.999999854372982[/C][/ROW]
[ROW][C]36[/C][C]2.84062364311938e-07[/C][C]5.68124728623876e-07[/C][C]0.999999715937636[/C][/ROW]
[ROW][C]37[/C][C]3.65361132474051e-07[/C][C]7.30722264948103e-07[/C][C]0.999999634638868[/C][/ROW]
[ROW][C]38[/C][C]3.85621805641011e-07[/C][C]7.71243611282022e-07[/C][C]0.999999614378194[/C][/ROW]
[ROW][C]39[/C][C]3.06762615661152e-07[/C][C]6.13525231322305e-07[/C][C]0.999999693237384[/C][/ROW]
[ROW][C]40[/C][C]2.35309129812231e-07[/C][C]4.70618259624462e-07[/C][C]0.99999976469087[/C][/ROW]
[ROW][C]41[/C][C]1.66512336136506e-07[/C][C]3.33024672273013e-07[/C][C]0.999999833487664[/C][/ROW]
[ROW][C]42[/C][C]1.1690093419261e-07[/C][C]2.3380186838522e-07[/C][C]0.999999883099066[/C][/ROW]
[ROW][C]43[/C][C]8.43651206713238e-08[/C][C]1.68730241342648e-07[/C][C]0.99999991563488[/C][/ROW]
[ROW][C]44[/C][C]6.28265197535801e-08[/C][C]1.25653039507160e-07[/C][C]0.99999993717348[/C][/ROW]
[ROW][C]45[/C][C]4.68115822839482e-08[/C][C]9.36231645678964e-08[/C][C]0.999999953188418[/C][/ROW]
[ROW][C]46[/C][C]3.32534873483773e-08[/C][C]6.65069746967545e-08[/C][C]0.999999966746513[/C][/ROW]
[ROW][C]47[/C][C]2.44525035962369e-08[/C][C]4.89050071924739e-08[/C][C]0.999999975547496[/C][/ROW]
[ROW][C]48[/C][C]1.90830671003401e-08[/C][C]3.81661342006802e-08[/C][C]0.999999980916933[/C][/ROW]
[ROW][C]49[/C][C]1.55759856972772e-08[/C][C]3.11519713945544e-08[/C][C]0.999999984424014[/C][/ROW]
[ROW][C]50[/C][C]1.39210980960704e-08[/C][C]2.78421961921409e-08[/C][C]0.999999986078902[/C][/ROW]
[ROW][C]51[/C][C]1.30911604925300e-08[/C][C]2.61823209850601e-08[/C][C]0.99999998690884[/C][/ROW]
[ROW][C]52[/C][C]1.35130485479267e-08[/C][C]2.70260970958534e-08[/C][C]0.999999986486951[/C][/ROW]
[ROW][C]53[/C][C]1.51758859736619e-08[/C][C]3.03517719473238e-08[/C][C]0.999999984824114[/C][/ROW]
[ROW][C]54[/C][C]1.87734261297878e-08[/C][C]3.75468522595755e-08[/C][C]0.999999981226574[/C][/ROW]
[ROW][C]55[/C][C]2.59113568543129e-08[/C][C]5.18227137086258e-08[/C][C]0.999999974088643[/C][/ROW]
[ROW][C]56[/C][C]4.38888268330831e-08[/C][C]8.77776536661662e-08[/C][C]0.999999956111173[/C][/ROW]
[ROW][C]57[/C][C]8.63113297212975e-08[/C][C]1.72622659442595e-07[/C][C]0.99999991368867[/C][/ROW]
[ROW][C]58[/C][C]1.97184110097417e-07[/C][C]3.94368220194834e-07[/C][C]0.99999980281589[/C][/ROW]
[ROW][C]59[/C][C]5.53526236840371e-07[/C][C]1.10705247368074e-06[/C][C]0.999999446473763[/C][/ROW]
[ROW][C]60[/C][C]1.42112839320398e-06[/C][C]2.84225678640796e-06[/C][C]0.999998578871607[/C][/ROW]
[ROW][C]61[/C][C]3.4747544798025e-06[/C][C]6.949508959605e-06[/C][C]0.99999652524552[/C][/ROW]
[ROW][C]62[/C][C]9.44636004492853e-06[/C][C]1.88927200898571e-05[/C][C]0.999990553639955[/C][/ROW]
[ROW][C]63[/C][C]2.67072750991714e-05[/C][C]5.34145501983428e-05[/C][C]0.9999732927249[/C][/ROW]
[ROW][C]64[/C][C]8.23418052466462e-05[/C][C]0.000164683610493292[/C][C]0.999917658194753[/C][/ROW]
[ROW][C]65[/C][C]0.000227043663561289[/C][C]0.000454087327122578[/C][C]0.999772956336439[/C][/ROW]
[ROW][C]66[/C][C]0.000672716691635665[/C][C]0.00134543338327133[/C][C]0.999327283308364[/C][/ROW]
[ROW][C]67[/C][C]0.00153461281846401[/C][C]0.00306922563692802[/C][C]0.998465387181536[/C][/ROW]
[ROW][C]68[/C][C]0.00352614515032068[/C][C]0.00705229030064136[/C][C]0.99647385484968[/C][/ROW]
[ROW][C]69[/C][C]0.00780305793073897[/C][C]0.0156061158614779[/C][C]0.99219694206926[/C][/ROW]
[ROW][C]70[/C][C]0.0171364262651728[/C][C]0.0342728525303457[/C][C]0.982863573734827[/C][/ROW]
[ROW][C]71[/C][C]0.0318580767610296[/C][C]0.0637161535220592[/C][C]0.96814192323897[/C][/ROW]
[ROW][C]72[/C][C]0.0523035936331341[/C][C]0.104607187266268[/C][C]0.947696406366866[/C][/ROW]
[ROW][C]73[/C][C]0.0774208224617841[/C][C]0.154841644923568[/C][C]0.922579177538216[/C][/ROW]
[ROW][C]74[/C][C]0.0956097089205235[/C][C]0.191219417841047[/C][C]0.904390291079477[/C][/ROW]
[ROW][C]75[/C][C]0.114319933388210[/C][C]0.228639866776420[/C][C]0.88568006661179[/C][/ROW]
[ROW][C]76[/C][C]0.133474719218696[/C][C]0.266949438437392[/C][C]0.866525280781304[/C][/ROW]
[ROW][C]77[/C][C]0.153391587934711[/C][C]0.306783175869421[/C][C]0.84660841206529[/C][/ROW]
[ROW][C]78[/C][C]0.173632581358604[/C][C]0.347265162717208[/C][C]0.826367418641396[/C][/ROW]
[ROW][C]79[/C][C]0.196962651703467[/C][C]0.393925303406934[/C][C]0.803037348296533[/C][/ROW]
[ROW][C]80[/C][C]0.21814319407973[/C][C]0.43628638815946[/C][C]0.78185680592027[/C][/ROW]
[ROW][C]81[/C][C]0.242372222334677[/C][C]0.484744444669354[/C][C]0.757627777665323[/C][/ROW]
[ROW][C]82[/C][C]0.265427968078179[/C][C]0.530855936156358[/C][C]0.734572031921821[/C][/ROW]
[ROW][C]83[/C][C]0.281593764256394[/C][C]0.563187528512789[/C][C]0.718406235743606[/C][/ROW]
[ROW][C]84[/C][C]0.299429126214282[/C][C]0.598858252428563[/C][C]0.700570873785718[/C][/ROW]
[ROW][C]85[/C][C]0.313175781983826[/C][C]0.626351563967652[/C][C]0.686824218016174[/C][/ROW]
[ROW][C]86[/C][C]0.329119486359628[/C][C]0.658238972719255[/C][C]0.670880513640372[/C][/ROW]
[ROW][C]87[/C][C]0.334859333252534[/C][C]0.669718666505068[/C][C]0.665140666747466[/C][/ROW]
[ROW][C]88[/C][C]0.340817706638682[/C][C]0.681635413277365[/C][C]0.659182293361318[/C][/ROW]
[ROW][C]89[/C][C]0.34365874710204[/C][C]0.68731749420408[/C][C]0.65634125289796[/C][/ROW]
[ROW][C]90[/C][C]0.358229112206268[/C][C]0.716458224412536[/C][C]0.641770887793732[/C][/ROW]
[ROW][C]91[/C][C]0.421101484740138[/C][C]0.842202969480276[/C][C]0.578898515259862[/C][/ROW]
[ROW][C]92[/C][C]0.439392261215678[/C][C]0.878784522431356[/C][C]0.560607738784322[/C][/ROW]
[ROW][C]93[/C][C]0.480923902889451[/C][C]0.961847805778901[/C][C]0.519076097110549[/C][/ROW]
[ROW][C]94[/C][C]0.44628549964359[/C][C]0.89257099928718[/C][C]0.55371450035641[/C][/ROW]
[ROW][C]95[/C][C]0.442206505514392[/C][C]0.884413011028784[/C][C]0.557793494485608[/C][/ROW]
[ROW][C]96[/C][C]0.413191176383495[/C][C]0.82638235276699[/C][C]0.586808823616505[/C][/ROW]
[ROW][C]97[/C][C]0.460127606376481[/C][C]0.920255212752962[/C][C]0.539872393623519[/C][/ROW]
[ROW][C]98[/C][C]0.613426452213427[/C][C]0.773147095573146[/C][C]0.386573547786573[/C][/ROW]
[ROW][C]99[/C][C]0.700071629674654[/C][C]0.599856740650692[/C][C]0.299928370325346[/C][/ROW]
[ROW][C]100[/C][C]0.790980211169657[/C][C]0.418039577660685[/C][C]0.209019788830343[/C][/ROW]
[ROW][C]101[/C][C]0.949735211381868[/C][C]0.100529577236265[/C][C]0.0502647886181324[/C][/ROW]
[ROW][C]102[/C][C]0.994238534688944[/C][C]0.011522930622113[/C][C]0.0057614653110565[/C][/ROW]
[ROW][C]103[/C][C]0.994712106824188[/C][C]0.0105757863516245[/C][C]0.00528789317581225[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57761&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57761&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.003865875398638120.007731750797276230.996134124601362
60.000833192642823470.001666385285646940.999166807357177
70.0001042053944688020.0002084107889376050.999895794605531
81.76408811240380e-053.52817622480760e-050.999982359118876
94.83299699929965e-069.6659939985993e-060.999995167003001
101.03730720163209e-062.07461440326419e-060.999998962692798
111.9017653217698e-073.8035306435396e-070.999999809823468
122.00468676949373e-074.00937353898745e-070.999999799531323
131.22150316652121e-072.44300633304242e-070.999999877849683
143.8681971028116e-087.7363942056232e-080.99999996131803
158.5218669285961e-091.70437338571922e-080.999999991478133
164.41973977526035e-098.8394795505207e-090.99999999558026
173.53411828546817e-097.06823657093634e-090.999999996465882
183.69187023893153e-097.38374047786306e-090.99999999630813
192.35486569933385e-084.70973139866771e-080.999999976451343
203.51800137012001e-087.03600274024001e-080.999999964819986
211.44502534467465e-082.8900506893493e-080.999999985549747
225.7655327651551e-091.15310655303102e-080.999999994234467
232.00116463288809e-094.00232926577618e-090.999999997998835
247.05926352202411e-101.41185270440482e-090.999999999294074
252.46950405100206e-104.93900810200411e-100.99999999975305
261.00413978200115e-102.00827956400231e-100.999999999899586
277.73114166708188e-111.54622833341638e-100.999999999922689
281.7140715224743e-103.4281430449486e-100.999999999828593
295.327347041672e-101.0654694083344e-090.999999999467265
301.85492649864778e-093.70985299729557e-090.999999998145074
311.40870654481176e-082.81741308962351e-080.999999985912934
323.35277371633315e-086.70554743266631e-080.999999966472263
335.15867452475614e-081.03173490495123e-070.999999948413255
346.39256096138433e-081.27851219227687e-070.99999993607439
351.45627018457859e-072.91254036915719e-070.999999854372982
362.84062364311938e-075.68124728623876e-070.999999715937636
373.65361132474051e-077.30722264948103e-070.999999634638868
383.85621805641011e-077.71243611282022e-070.999999614378194
393.06762615661152e-076.13525231322305e-070.999999693237384
402.35309129812231e-074.70618259624462e-070.99999976469087
411.66512336136506e-073.33024672273013e-070.999999833487664
421.1690093419261e-072.3380186838522e-070.999999883099066
438.43651206713238e-081.68730241342648e-070.99999991563488
446.28265197535801e-081.25653039507160e-070.99999993717348
454.68115822839482e-089.36231645678964e-080.999999953188418
463.32534873483773e-086.65069746967545e-080.999999966746513
472.44525035962369e-084.89050071924739e-080.999999975547496
481.90830671003401e-083.81661342006802e-080.999999980916933
491.55759856972772e-083.11519713945544e-080.999999984424014
501.39210980960704e-082.78421961921409e-080.999999986078902
511.30911604925300e-082.61823209850601e-080.99999998690884
521.35130485479267e-082.70260970958534e-080.999999986486951
531.51758859736619e-083.03517719473238e-080.999999984824114
541.87734261297878e-083.75468522595755e-080.999999981226574
552.59113568543129e-085.18227137086258e-080.999999974088643
564.38888268330831e-088.77776536661662e-080.999999956111173
578.63113297212975e-081.72622659442595e-070.99999991368867
581.97184110097417e-073.94368220194834e-070.99999980281589
595.53526236840371e-071.10705247368074e-060.999999446473763
601.42112839320398e-062.84225678640796e-060.999998578871607
613.4747544798025e-066.949508959605e-060.99999652524552
629.44636004492853e-061.88927200898571e-050.999990553639955
632.67072750991714e-055.34145501983428e-050.9999732927249
648.23418052466462e-050.0001646836104932920.999917658194753
650.0002270436635612890.0004540873271225780.999772956336439
660.0006727166916356650.001345433383271330.999327283308364
670.001534612818464010.003069225636928020.998465387181536
680.003526145150320680.007052290300641360.99647385484968
690.007803057930738970.01560611586147790.99219694206926
700.01713642626517280.03427285253034570.982863573734827
710.03185807676102960.06371615352205920.96814192323897
720.05230359363313410.1046071872662680.947696406366866
730.07742082246178410.1548416449235680.922579177538216
740.09560970892052350.1912194178410470.904390291079477
750.1143199333882100.2286398667764200.88568006661179
760.1334747192186960.2669494384373920.866525280781304
770.1533915879347110.3067831758694210.84660841206529
780.1736325813586040.3472651627172080.826367418641396
790.1969626517034670.3939253034069340.803037348296533
800.218143194079730.436286388159460.78185680592027
810.2423722223346770.4847444446693540.757627777665323
820.2654279680781790.5308559361563580.734572031921821
830.2815937642563940.5631875285127890.718406235743606
840.2994291262142820.5988582524285630.700570873785718
850.3131757819838260.6263515639676520.686824218016174
860.3291194863596280.6582389727192550.670880513640372
870.3348593332525340.6697186665050680.665140666747466
880.3408177066386820.6816354132773650.659182293361318
890.343658747102040.687317494204080.65634125289796
900.3582291122062680.7164582244125360.641770887793732
910.4211014847401380.8422029694802760.578898515259862
920.4393922612156780.8787845224313560.560607738784322
930.4809239028894510.9618478057789010.519076097110549
940.446285499643590.892570999287180.55371450035641
950.4422065055143920.8844130110287840.557793494485608
960.4131911763834950.826382352766990.586808823616505
970.4601276063764810.9202552127529620.539872393623519
980.6134264522134270.7731470955731460.386573547786573
990.7000716296746540.5998567406506920.299928370325346
1000.7909802111696570.4180395776606850.209019788830343
1010.9497352113818680.1005295772362650.0502647886181324
1020.9942385346889440.0115229306221130.0057614653110565
1030.9947121068241880.01057578635162450.00528789317581225







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level640.646464646464647NOK
5% type I error level680.686868686868687NOK
10% type I error level690.696969696969697NOK

\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 & 64 & 0.646464646464647 & NOK \tabularnewline
5% type I error level & 68 & 0.686868686868687 & NOK \tabularnewline
10% type I error level & 69 & 0.696969696969697 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57761&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]64[/C][C]0.646464646464647[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]68[/C][C]0.686868686868687[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]69[/C][C]0.696969696969697[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57761&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57761&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 level640.646464646464647NOK
5% type I error level680.686868686868687NOK
10% type I error level690.696969696969697NOK



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